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A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients.

The massive and continuous spread of COVID-19 has motivated researchers around the world to intensely explore, understand, and develop new techniques for diagnosis and treatment. Although lung ultrasound imaging is a less established approach when compared to other medical imaging modalities such as X-ray and CT, multiple studies have demonstrated its promise to diagnose COVID-19 patients. At the same time, many deep learning models have been built to improve the diagnostic efficiency of medical imaging. The integration of these initially parallel efforts has led multiple researchers to report deep learning applications in medical imaging of COVID-19 patients, most of which demonstrate the outstanding potential of deep learning to aid in the diagnosis of COVID-19. This invited review is focused on deep learning applications in lung ultrasound imaging of COVID-19 and provides a comprehensive overview of ultrasound systems utilized for data acquisition, associated datasets, deep learning models, and comparative performance.

BME frontiers

10.34133/2022/9780173

Lightweight ResGRU: a deep learning-based prediction of SARS-CoV-2 (COVID-19) and its severity classification using multimodal chest radiography images.

The new COVID-19 emerged in a town in China named Wuhan in December 2019, and since then, this deadly virus has infected 324 million people worldwide and caused 5.53 million deaths by January 2022. Because of the rapid spread of this pandemic, different countries are facing the problem of a shortage of resources, such as medical test kits and ventilators, as the number of cases increased uncontrollably. Therefore, developing a readily available, low-priced, and automated approach for COVID-19 identification is the need of the hour. The proposed study uses chest radiography images (CRIs) such as X-rays and computed tomography (CTs) to detect chest infections, as these modalities contain important information about chest infections. This research introduces a novel hybrid deep learning model named

Neural computing & applications

10.1007/s00521-023-08200-0 10.1016/S0140-6736(20)30183-5 10.1038/s41586-020-2008-3 10.1007/978-3-030-60188-1_2 10.1016/j.chaos.2020.110495 10.1016/j.chaos.2021.110713 10.1016/S1473-3099(20)30134-1 10.1016/j.bea.2021.100003 10.2217/fmb-2020-0098 10.1016/S0140-6736(20)30154-9 10.1148/ryct.2020200028 10.1148/radiol.2020201473 10.1148/ryct.2020200213 10.3389/fnins.2021.601109 10.1002/widm.1312 10.1145/3065386 10.1109/CVPR.2016.90 10.1051/matecconf/201927702001 10.1016/j.compbiomed.2020.103795 10.1016/j.imu.2020.100405 10.1016/j.ejrad.2020.109402 10.1007/s00330-021-07715-1 10.1016/j.eswa.2020.114054 10.1007/s11042-021-11388-9 10.1016/j.compbiomed.2020.103792 10.1016/j.imu.2020.100412 10.1016/j.asoc.2021.107160 10.1007/s10489-020-01888-w 10.1007/s42600-021-00151-6 10.1016/j.ijleo.2021.166405 10.1038/s41568-020-00327-9 10.1016/j.compmedimag.2019.05.001 10.1038/nature14539 10.1080/07391102.2020.1767212 10.1016/j.bspc.2021.102490 10.1016/j.patcog.2021.108255

COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet.

Coronavirus disease 2019 (COVID-19) epidemic has devastating effects on personal health around the world. It is significant to achieve accurate segmentation of pulmonary infection regions, which is an early indicator of disease. To solve this problem, a deep learning model, namely, the content-aware pre-activated residual UNet (CAPA-ResUNet), was proposed for segmenting COVID-19 lesions from CT slices. In this network, the pre-activated residual block was used for down-sampling to solve the problems of complex foreground and large fluctuations of distribution in datasets during training and to avoid gradient disappearance. The area loss function based on the false segmentation regions was proposed to solve the problem of fuzzy boundary of the lesion area. This model was evaluated by the public dataset (COVID-19 Lung CT Lesion Segmentation Challenge-2020) and compared its performance with those of classical models. Our method gains an advantage over other models in multiple metrics. Such as the Dice coefficient, specificity (Spe), and intersection over union (IoU), our CAPA-ResUNet obtained 0.775 points, 0.972 points, and 0.646 points, respectively. The Dice coefficient of our model was 2.51% higher than Content-aware residual UNet (CARes-UNet). The code is available at https://github.com/malu108/LungInfectionSeg.

International journal of imaging systems and technology

10.1002/ima.22819

Artificial Intelligence in Paediatric Tuberculosis.

Tuberculosis (TB) continues to be a leading cause of death in children despite global efforts focused on early diagnosis and interventions to limit the spread of the disease. This challenge has been made more complex in the context of the coronavirus pandemic, which has disrupted the "End TB Strategy" and framework set out by the World Health Organization (WHO). Since the inception of artificial intelligence (AI) more than 60 years ago, the interest in AI has risen and more recently we have seen the emergence of multiple real-world applications, many of which relate to medical imaging. Nonetheless, real-world AI applications and clinical studies are limited in the niche area of paediatric imaging. This review article will focus on how AI, or more specifically deep learning, can be applied to TB diagnosis and management in children. We describe how deep learning can be utilised in chest imaging to provide computer-assisted diagnosis to augment workflow and screening efforts. We also review examples of recent AI applications for TB screening in resource constrained environments and we explore some of the challenges and the future directions of AI in paediatric TB.

Pediatric radiology

10.1007/s00247-023-05606-9 10.7754/Clin.Lab.2015.150509 10.21037/jtd-21-1342 10.1093/cid/ciac011 10.1155/2014/291841 10.1097/INF.0000000000000792 10.1136/adc.2004.062315 10.5588/ijtld.15.0201 10.5588/ijtld.18.0122 10.1007/s00247-017-3866-1 10.1007/s00247-020-04625-0 10.1164/rccm.202202-0259OC 10.1093/cid/ciab708 10.1038/s41598-021-03265-0 10.1007/s00330-020-07024-z 10.1155/2021/5359084 10.1016/S2589-7500(20)30221-1 10.1148/radiol.2017162326 10.1007/s00330-020-07219-4 10.1148/radiol.2021210063 10.1038/s41598-021-93967-2 10.3389/fmolb.2022.874475 10.1016/S2589-7500(21)00116-3 10.1038/s41598-019-51503-3 10.1038/s41746-020-0273-z 10.1093/cid/ciab639 10.1007/s00259-021-05432-x 10.3389/frai.2022.827299 10.1007/s00330-021-08365-z 10.1016/j.clinimag.2022.04.009 10.21037/qims-21-676 10.1148/radiol.2018181422 10.1038/s41591-018-0307-0 10.1371/journal.pone.0212094 10.1007/s00247-021-05146-0 10.1093/cid/ciy967 10.1371/journal.pone.0221339 10.5588/ijtld.17.0520 10.1038/s41746-021-00393-9 10.3389/fdata.2022.850383 10.1007/s00247-019-04593-0 10.1038/s41598-020-73831-5 10.1371/journal.pone.0206410 10.1097/INF.0000000000001872 10.1002/ppul.24230 10.1002/ppul.24500 10.1007/s00247-017-3895-9 10.1038/srep12215 10.1038/s41598-020-62148-y

Towards precision medicine: Omics approach for COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human society. Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19. Here, we reviewed how omics, including genomics, proteomics, single-cell multi-omics, and clinical phenomics, play roles in answering biological and clinical questions about COVID-19. Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification. Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement. Furthermore, decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.

Biosafety and health

10.1016/j.bsheal.2023.01.002 10.1073/pnas.0408290102 10.1038/s41588-022-01033-y 10.1126/scitranslmed.abk3445 10.1126/science.abd7331 10.1126/science.abm1208 10.1016/j.gpb.2022.01.001 10.3389/fimmu.2021.622176 10.1038/s41588-021-00854-7 10.1038/s41421-021-00318-6 10.1056/nejmoa2020283 10.1038/s41586-021-03767-x 10.1038/s41588-021-00996-8 10.1038/s41588-021-00955-3 10.1038/s41588-021-00986-w 10.1038/s41588-022-01042-x 10.1038/s41588-021-01006-7 10.1038/s41586-022-04826-7 10.1038/s43587-021-00067-x 10.1016/j.cell.2021.01.004 10.1016/j.immuni.2020.10.008 10.1038/s41586-021-03493-4 10.1016/j.celrep.2021.110271 10.1038/s41467-021-27716-4 10.1038/s41467-021-24482-1 10.1016/j.cell.2022.01.012 10.1126/scitranslmed.abj7521 10.1038/s41586-020-2588-y 10.7554/eLife.62522 10.1084/jem.20210582 10.1016/j.immuni.2020.11.017 10.1016/j.cell.2020.10.037 10.1038/s41586-021-03570-8 10.1038/s42255-021-00425-4 10.1038/s41591-021-01329-2 10.1038/s41746-021-00399-3 10.1038/s41467-020-17971-2 10.1038/s41467-020-17280-8 10.1016/j.cell.2020.04.045 10.1038/s41591-020-0931-3 10.1038/s41551-020-00633-5 10.1016/j.xcrm.2022.100580 10.1080/10408363.2020.1851167 10.1002/mco2.90 10.1038/s41587-021-01131-y 10.1016/j.xinn.2022.100289 10.1055/s-0040-1712549 10.1093/cid/ciab754 10.1080/14760584.2021.1976153 10.1002/jmv.27524 10.1038/s41392-022-01105-9 10.1038/s41586-021-04188-6 10.1002/mco2.110 10.2807/1560-7917.ES.2021.26.24.2100509 10.46234/ccdcw2021.255 10.1016/j.gpb.2020.09.001 10.1093/nar/gkw1065 10.1093/ve/veab064 10.1126/science.abe3261 10.1126/science.abc0523 10.1126/science.abb9263 10.1038/s41467-022-31511-0 10.1038/s41467-020-18314-x 10.1126/scitranslmed.abn7979 10.1126/science.abp8337 10.1038/s41467-020-19345-0 10.1038/s41591-020-0997-y 10.1038/s41591-020-1000-7 10.1126/science.abq5358 10.1038/s41576-022-00483-8 10.1016/S1473-3099(21)00170-5 10.1016/S2468-2667(21)00055-4 10.1016/j.cell.2020.11.020 10.7554/eLife.65365 10.1038/s41576-021-00408-x 10.1038/s41586-021-04352-y 10.1038/s41586-021-03792-w 10.1089/omi.2021.0182 10.1021/acs.jproteome.1c00475 10.1039/d0cb00163e 10.1038/s41746-021-00431-6 10.1038/s42256-021-00307-0 10.1038/s42256-021-00377-0 10.1038/s41591-022-01843-x 10.1089/jwh.2021.0411 10.1001/jamanetworkopen.2021.47053 10.1016/S0140-6736(22)00941-2 10.1038/s41591-022-01840-0 10.1001/jamapsychiatry.2022.2640 10.1016/j.cell.2022.01.014 10.1016/j.immuni.2022.01.017 10.1126/sciimmunol.abk1741 10.1038/s41591-022-01837-9 10.3389/fimmu.2022.838132 10.1126/sciimmunol.abm7996 10.1136/bmj.o407 10.1038/s41586-020-2355-0 10.1016/j.jval.2021.10.007 10.1038/d41586-022-03181-x

DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays.

Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety, and it is anticipated that deep learning (DL) will be the most effective way of detecting COVID-19 and other chest diseases such as lung cancer (LC), tuberculosis (TB), pneumothorax (PneuTh), and pneumonia (Pneu). However, data sharing across hospitals is hampered by patients' right to privacy, leading to unexpected results from deep neural network (DNN) models. Federated learning (FL) is a game-changing concept since it allows clients to train models together without sharing their source data with anybody else. Few studies, however, focus on improving the model's accuracy and stability, whereas most existing FL-based COVID-19 detection techniques aim to maximize secondary objectives such as latency, energy usage, and privacy. In this work, we design a novel model named decision-making-based federated learning network (DMFL_Net) for medical diagnostic image analysis to distinguish COVID-19 from four distinct chest disorders including LC, TB, PneuTh, and Pneu. The DMFL_Net model that has been suggested gathers data from a variety of hospitals, constructs the model using the DenseNet-169, and produces accurate predictions from information that is kept secure and only released to authorized individuals. Extensive experiments were carried out with chest X-rays (CXR), and the performance of the proposed model was compared with two transfer learning (TL) models, i.e., VGG-19 and VGG-16 in terms of accuracy (ACC), precision (PRE), recall (REC), specificity (SPF), and F1-measure. Additionally, the DMFL_Net model is also compared with the default FL configurations. The proposed DMFL_Net + DenseNet-169 model achieves an accuracy of 98.45% and outperforms other approaches in classifying COVID-19 from four chest diseases and successfully protects the privacy of the data among diverse clients.

Sensors (Basel, Switzerland)

10.3390/s23020743 10.3390/electronics11172714 10.1109/OJCS.2022.3206407 10.3390/life12070958 10.1002/int.22777 10.1109/TCBB.2022.3184319 10.1109/ACCESS.2020.3037474 10.1038/s41746-020-00323-1 10.1109/bigdata50022.2020.9377873 10.3389/fpubh.2022.892499 10.1109/JIOT.2021.3056185 10.1109/JIOT.2021.3120998 10.1145/3501296 10.1016/j.asoc.2021.107330 10.1038/s41591-021-01506-3 10.2196/24207 10.1007/978-3-030-11723-8_9 10.1109/JIOT.2019.2956615 10.1109/isbi.2019.8759317 10.1145/3528580.3532845 10.1109/JBHI.2022.3143576 10.1109/icic53490.2021.9693006 10.1007/s00530-021-00878-3 10.1371/journal.pone.0266462 10.18280/ijdne.170106 10.1007/978-981-16-7618-5_13 10.14419/ijet.v9i3.30655 10.1007/s11042-022-13843-7 10.1007/s10796-022-10307-z 10.3233/shti220697 10.1109/JIOT.2022.3144450 10.1016/j.compbiomed.2022.105233 10.1109/TMM.2018.2889934 10.1109/JIOT.2019.2920987 10.1109/ACCESS.2018.2885997 10.1016/j.asoc.2020.106859 10.1016/j.knosys.2021.106775 10.32604/cmc.2022.020344 10.1016/j.dib.2020.106520 10.1111/exsy.13173 10.1109/ACCESS.2021.3102399 10.1145/3431804 10.32604/cmc.2021.013191 10.1007/s11042-022-13499-3 10.3390/diagnostics11091735 10.1001/jamanetworkopen.2019.1095 10.1016/j.cell.2018.02.010 10.2214/ajr.174.1.1740071 10.1016/j.fcij.2017.12.001 10.1109/ACCESS.2020.3031384 10.1038/s41598-020-76282-0 10.26599/TST.2021.9010026 10.1109/ubmk52708.2021.9558913 10.1109/JBHI.2020.3005160 10.1016/j.bspc.2021.102588 10.1016/j.patrec.2020.09.010 10.1111/exsy.12759 10.1146/annurev-bioeng-110220-012203 10.2174/1573405617666210414101941 10.1007/s42600-021-00135-6 10.3390/s22155652 10.1016/j.jpha.2021.12.006 10.1007/s00530-021-00826-1 10.3390/jpm12020275 10.1002/wcms.1597 10.1109/ACCESS.2020.3001507 10.1007/s40747-022-00866-8

Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images.

The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people's health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively.

International journal of environmental research and public health

10.3390/ijerph20021268 10.1016/j.jds.2020.02.002 10.1016/j.ajem.2020.03.036 10.1007/s11042-021-10714-5 10.1093/aje/kwab093 10.1038/s41597-021-00900-3 10.1038/s41598-020-76282-0 10.1007/s11547-019-00990-5 10.1007/s11547-020-01135-9 10.1007/s11547-020-01277-w 10.1007/s12559-020-09773-x 10.1109/TNNLS.2018.2790388 10.1016/j.jksuci.2020.03.013 10.1016/j.chaos.2020.110190 10.1016/S2589-7500(21)00039-X 10.1007/s00500-020-05424-3 10.1016/j.bbe.2021.05.013 10.1155/2021/6658058 10.1016/j.chaos.2020.109944 10.3389/frai.2021.694875 10.1016/j.cmpb.2020.105581 10.1101/2020.03.26.20044610 10.3390/app11083414 10.3390/healthcare10020343 10.17632/8h65ywd2jr.3 10.1155/2021/5587188 10.1038/s41598-021-86735-9 10.1109/72.788646 10.1088/1742-6596/1748/4/042054 10.1007/s11263-019-01228-7

Research on the Application of Artificial Intelligence in Public Health Management: Leveraging Artificial Intelligence to Improve COVID-19 CT Image Diagnosis.

Since the start of 2020, the outbreak of the Coronavirus disease (COVID-19) has been a global public health emergency, and it has caused unprecedented economic and social disaster. In order to improve the diagnosis efficiency of COVID-19 patients, a number of researchers have conducted extensive studies on applying artificial intelligence techniques to the analysis of COVID-19-related medical images. The automatic segmentation of lesions from computed tomography (CT) images using deep learning provides an important basis for the quantification and diagnosis of COVID-19 cases. For a deep learning-based CT diagnostic method, a few of accurate pixel-level labels are essential for the training process of a model. However, the translucent ground-glass area of the lesion usually leads to mislabeling while performing the manual labeling operation, which weakens the accuracy of the model. In this work, we propose a method for correcting rough labels; that is, to hierarchize these rough labels into precise ones by performing an analysis on the pixel distribution of the infected and normal areas in the lung. The proposed method corrects the incorrectly labeled pixels and enables the deep learning model to learn the infected degree of each infected pixel, with which an aiding system (named DLShelper) for COVID-19 CT image diagnosis using the hierarchical labels is also proposed. The DLShelper targets lesion segmentation from CT images, as well as the severity grading. The DLShelper assists medical staff in efficient diagnosis by providing rich auxiliary diagnostic information (including the severity grade, the proportions of the lesion and the visualization of the lesion area). A comprehensive experiment based on a public COVID-19 CT image dataset is also conducted, and the experimental results show that the DLShelper significantly improves the accuracy of segmentation for the lesion areas and also achieves a promising accuracy for the severity grading task.

International journal of environmental research and public health

10.3390/ijerph20021158 10.3390/diagnostics10110901 10.1007/s11548-021-02466-2 10.1148/radiol.2020200642 10.1016/j.chest.2020.06.025 10.1109/RBME.2020.2987975 10.1016/j.cell.2020.04.045 10.1109/TIP.2021.3058783 10.1609/aaai.v35i6.16617 10.1109/TMI.2020.2996645 10.1016/j.media.2018.11.010 10.1109/TMI.2020.3000314 10.1007/s00521-022-07709-0 10.1109/TMI.2017.2775636 10.1016/j.jvcir.2016.11.019 10.1186/s12938-015-0014-8 10.1109/TMI.2012.2196285 10.3389/fpubh.2022.1015876 10.1002/mp.12273 10.1016/j.cmpb.2019.06.005 10.1007/s10278-019-00254-8 10.1007/978-3-319-24574-4_28 10.1109/TPAMI.2016.2644615 10.1613/jair.953

COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach.

COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading nature among people, mainly through physical interactions. Therefore, it is very important to control the spreading of the virus and detect people's symptoms during the initial stages so proper preventive measures can be taken in good time. In response to COVID-19, revolutionary automation such as deep learning, machine learning, image processing, and medical images such as chest radiography (CXR) and computed tomography (CT) have been developed in this environment. Currently, the coronavirus is identified via an RT-PCR test. Alternative solutions are required due to the lengthy moratorium period and the large number of false-negative estimations. To prevent the spreading of the virus, we propose the Vehicle-based COVID-19 Detection System to reveal the related symptoms of a person in the vehicles. Moreover, deep extreme machine learning is applied. The proposed system uses headaches, flu, fever, cough, chest pain, shortness of breath, tiredness, nasal congestion, diarrhea, breathing difficulty, and pneumonia. The symptoms are considered parameters to reveal the presence of COVID-19 in a person. Our proposed approach in Vehicles will make it easier for governments to perform COVID-19 tests timely in cities. Due to the ambiguous nature of symptoms in humans, we utilize fuzzy modeling for simulation. The suggested COVID-19 detection model achieved an accuracy of more than 90%.

Diagnostics (Basel, Switzerland)

10.3390/diagnostics13020270 10.1056/NEJMoa2001017 10.46234/ccdcw2020.017 10.1016/S0140-6736(20)30154-9 10.1016/S0140-6736(20)30183-5 10.1017/ice.2020.61 10.9781/ijimai.2020.02.002 10.3390/jcm9030674 10.1136/jim-2020-001491 10.3390/diagnostics12040846 10.1007/s10140-021-01937-y 10.3390/diagnostics12071617 10.9781/ijimai.2018.04.003 10.1198/004017005000000058 10.3978/j.issn.2072-1439.2015.04.61 10.1088/1742-6596/892/1/012016 10.15837/ijccc.2010.3.2481 10.1016/j.artmed.2016.12.003 10.1080/00401706.1980.10486139 10.3390/app12094493 10.1007/s00330-021-07715-1 10.1038/s41598-020-76550-z 10.1109/ACCESS.2020.2976452 10.3233/AIS-200554 10.32604/cmc.2020.011155 10.1007/s13042-011-0019-y

Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning Approach.

Many people have been affected by infectious lung diseases (ILD). With the outbreak of the COVID-19 disease in the last few years, many people have waited for weeks to recover in the intensive care wards of hospitals. Therefore, early diagnosis of ILD is of great importance to reduce the occupancy rates of health institutions and the treatment time of patients. Many artificial intelligence-based studies have been carried out in detecting and classifying diseases from medical images using imaging applications. The most important goal of these studies was to increase classification performance and model reliability. In this approach, a powerful algorithm based on a new customized deep learning model (ACL model), which trained synchronously with the attention and LSTM model with CNN models, was proposed to classify healthy, COVID-19 and Pneumonia. The important stains and traces in the chest X-ray (CX-R) image were emphasized with the marker-controlled watershed (MCW) segmentation algorithm. The ACL model was trained for different training-test ratios (90-10%, 80-20%, and 70-30%). For 90-10%, 80-20%, and 70-30% training-test ratios, accuracy scores were 100%, 96%, and 96%, respectively. The best performance results were obtained compared to the existing methods. In addition, the contribution of the strategies utilized in the proposed model to classification performance was analyzed in detail. Deep learning-based applications can be used as a useful decision support tool for physicians in the early diagnosis of ILD diseases. However, for the reliability of these applications, it is necessary to undertake verification with many datasets.

Diagnostics (Basel, Switzerland)

10.3390/diagnostics13020260 10.1038/s41586-020-2008-3 10.1016/S0140-6736(20)30211-7 10.1148/radiol.2020200463 10.1148/radiol.2020200432 10.1007/s10044-021-00984-y 10.1038/s41598-020-76550-z 10.1016/j.chemolab.2020.104054 10.1016/j.aiia.2020.10.002 10.1001/jama.2018.19323 10.1002/emmm.201100182 10.1016/j.aiia.2020.09.002 10.1016/j.patrec.2020.09.010 10.33889/IJMEMS.2020.5.4.052 10.1007/s13246-020-00865-4 10.1109/ACCESS.2020.3016780 10.1016/j.compbiomed.2020.103792 10.1016/j.chaos.2020.110071 10.3892/etm.2020.8797 10.1016/j.asoc.2021.107160 10.1016/j.eswa.2020.114054 10.1016/j.asoc.2022.108610 10.1007/s00354-021-00152-0 10.14358/PERS.70.3.351 10.1109/JSTARS.2018.2830410 10.14569/IJACSA.2017.080853 10.3390/jpm12010055 10.1016/j.bspc.2022.103625 10.1016/j.bspc.2020.102194 10.1016/j.bbe.2021.07.004 10.3390/jpm11121276 10.1016/j.apacoust.2021.108260 10.1016/j.mehy.2020.109761 10.1016/j.asoc.2020.106580 10.1007/s10489-020-01888-w 10.1016/j.compbiomed.2020.103805

Deep Learning for Detecting COVID-19 Using Medical Images.

The global spread of COVID-19 (also known as SARS-CoV-2) is a major international public health crisis [...].

Bioengineering (Basel, Switzerland)

10.3390/bioengineering10010019 10.3390/bioengineering8070098 10.3390/bioengineering8040049 10.1016/j.media.2020.101794 10.1007/s00500-020-05424-3 10.1038/s41598-020-76550-z 10.1016/j.ins.2020.09.041 10.1016/j.neucom.2022.01.055 10.1109/TNNLS.2021.3114747 10.1016/j.media.2021.102299 10.1109/TMI.2020.2993291 10.1016/j.compbiomed.2022.105233 10.1109/TMI.2020.3040950 10.1016/j.compbiomed.2022.105732 10.1109/TNNLS.2022.3201198 10.1016/j.irbm.2020.05.003 10.1109/JBHI.2020.3023246 10.1109/JBHI.2020.3030853 10.1109/TCYB.2020.3042837 10.1109/TMI.2020.2995508 10.1109/TMI.2020.2994908 10.1016/j.media.2021.102105 10.1016/j.media.2020.101913 10.1109/TMI.2020.2996256 10.1109/TMI.2020.2995965 10.3390/bioengineering8020026

Classification of Pulmonary Damage Stages Caused by COVID-19 Disease from CT Scans via Transfer Learning.

The COVID-19 pandemic has produced social and economic changes that are still affecting our lives. The coronavirus is proinflammatory, it is replicating, and it is quickly spreading. The most affected organ is the lung, and the evolution of the disease can degenerate very rapidly from the early phase, also known as mild to moderate and even severe stages, where the percentage of recovered patients is very low. Therefore, a fast and automatic method to detect the disease stages for patients who underwent a computer tomography investigation can improve the clinical protocol. Transfer learning is used do tackle this issue, mainly by decreasing the computational time. The dataset is composed of images from public databases from 118 patients and new data from 55 patients collected during the COVID-19 spread in Romania in the spring of 2020. Even if the disease detection by the computerized tomography scans was studied using deep learning algorithms, to our knowledge, there are no studies related to the multiclass classification of the images into pulmonary damage stages. This could be helpful for physicians to automatically establish the disease severity and decide on the proper treatment for patients and any special surveillance, if needed. An evaluation study was completed by considering six different pre-trained CNNs. The results are encouraging, assuring an accuracy of around 87%. The clinical impact is still huge, even if the disease spread and severity are currently diminished.

Bioengineering (Basel, Switzerland)

10.3390/bioengineering10010006 10.1016/j.jacr.2020.02.008 10.1371/journal.pone.0235844 10.1148/ryct.2020200028 10.1148/radiol.2020200230 10.1007/s10916-020-01562-1 10.1016/j.patcog.2022.108538 10.1016/j.chaos.2020.109944 10.1142/S0218348X20501145 10.1007/s10489-020-01826-w 10.1016/j.bspc.2021.102588 10.1007/s10916-021-01707-w 10.1016/j.bbe.2021.04.006 10.1016/j.chaos.2020.110153 10.3390/e22050517 10.1016/j.bspc.2022.104250 10.3389/fonc.2020.01560 10.1056/NEJMoa2001316 10.1186/s43055-020-00236-9 10.1148/radiol.2020200463 10.2214/AJR.20.22976 10.1016/j.jormas.2019.06.002 10.1146/annurev-bioeng-071516-044442 10.1016/j.bspc.2021.103326 10.1186/s40537-019-0235-y 10.1186/s40537-021-00428-8

A Survey on Deep Learning in COVID-19 Diagnosis.

According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.

Journal of imaging

10.3390/jimaging9010001 10.1016/j.procbio.2020.08.016 10.1056/NEJMoa2002032 10.1016/j.asoc.2020.106580 10.1016/j.bios.2020.112349 10.1016/j.bios.2020.112437 10.1021/acsnano.0c02439 10.1016/j.cartre.2020.100011 10.1148/radiol.2020201237 10.1148/radiol.2020200343 10.1016/S0140-6736(20)30154-9 10.1186/s12938-020-00831-x 10.1016/j.neucom.2020.05.078 10.1007/s10462-021-09985-z 10.1016/j.aej.2021.07.007 10.1016/j.eswa.2017.08.006 10.1186/s41747-018-0061-6 10.1142/S0129065718500582 10.1049/cit2.12042 10.3348/kjr.2017.18.4.570 10.1038/s41746-022-00592-y 10.20517/ais.2021.15 10.1049/cit2.12060 10.1038/nature14539 10.1049/cit2.12059 10.1002/ett.4080 10.1016/j.job.2022.03.003 10.1016/j.bspc.2021.103165 10.1109/TIP.2005.852470 10.1016/j.neunet.2012.02.023 10.1007/s00521-021-06762-5 10.1134/S1054661822020110 10.1145/3507902 10.3390/su14031447 10.1155/2022/1830010 10.1016/j.media.2021.102311 10.1016/j.compbiomed.2022.105244 10.1007/s42600-020-00120-5 10.1016/j.displa.2022.102150 10.1259/0007-1285-46-552-1016 10.1007/s11604-020-01010-7 10.1016/j.crad.2020.04.001 10.1148/radiol.2020200463 10.1148/radiol.2020201160 10.1016/j.jacr.2018.09.012 10.21037/atm.2017.07.20 10.1177/0846537120924606 10.1109/TMI.2020.2993291 10.3390/jimaging8010002 10.1109/TIP.2017.2713099 10.1016/j.patcog.2017.10.013 10.1007/s13369-020-04758-2 10.1145/3065386 10.1016/j.neunet.2015.07.007 10.1016/j.cmpb.2019.05.004 10.1016/j.knosys.2020.106396 10.1016/j.swevo.2021.100863 10.3390/app12178643 10.1016/j.powtec.2022.117409 10.1016/j.ymssp.2017.06.022 10.32604/cmc.2022.020140 10.3389/fpubh.2021.726144 10.1016/j.patrec.2021.02.005 10.1016/j.patrec.2020.04.018 10.1016/j.jksuci.2021.05.001 10.1016/j.neucom.2020.06.117 10.1111/nph.16830 10.1016/j.neucom.2018.03.080 10.1007/s12145-019-00383-2 10.1155/2021/6633755 10.1109/JSEN.2020.3025855 10.1016/j.jksuci.2021.07.005 10.1186/s40537-019-0197-0 10.1016/j.compbiomed.2021.104375 10.1016/j.bspc.2021.103326 10.1364/OL.390026 10.1016/j.inffus.2021.07.001 10.1364/BOE.10.006145 10.1097/JU.0000000000000852.020 10.3389/frobt.2019.00144 10.1007/s11042-017-5243-3 10.1109/TCSVT.2019.2935128 10.1167/16.12.326 10.1109/ACCESS.2017.2696121 10.1109/5.726791 10.1109/72.279181 10.1186/s40537-016-0043-6 10.1109/TKDE.2009.191 10.1023/A:1007379606734 10.1613/jair.1872 10.1016/S0378-3758(00)00115-4 10.21037/jtd-21-747 10.3390/sym14071310 10.1016/j.asoc.2020.106912 10.1007/s11390-020-0679-8 10.1016/j.eng.2020.04.010 10.1016/j.ejrad.2020.109041 10.1016/j.bspc.2021.102588 10.1007/s00521-020-05437-x 10.1371/journal.pone.0259179 10.1007/s42979-021-00782-7 10.1109/TCYB.2020.3042837 10.1007/s10140-020-01886-y 10.1007/s13755-021-00140-0 10.1109/JSEN.2021.3062442 10.3844/jcssp.2020.620.625 10.1007/s00138-020-01128-8 10.1038/s41598-020-74164-z 10.1016/j.bspc.2021.102987 10.1109/TLA.2021.9451239 10.1155/2021/8829829 10.1007/s10044-021-00984-y 10.1109/ACCESS.2020.3010287 10.1007/s10489-020-02055-x 10.1088/1361-6501/ac8ca4 10.1016/j.bspc.2021.102814 10.1101/2022.03.13.22272311 10.32628/IJSRST207614 10.1016/j.ijmedinf.2020.104284

Pandemic disease detection through wireless communication using infrared image based on deep learning.

Rapid diagnosis to test diseases, such as COVID-19, is a significant issue. It is a routine virus test in a reverse transcriptase-polymerase chain reaction. However, a test like this takes longer to complete because it follows the serial testing method, and there is a high chance of a false-negative ratio (FNR). Moreover, there arises a deficiency of R.T.-PCR test kits. Therefore, alternative procedures for a quick and accurate diagnosis of patients are urgently needed to deal with these pandemics. The infrared image is self-sufficient for detecting these diseases by measuring the temperature at the initial stage. C.T. scans and other pathological tests are valuable aspects of evaluating a patient with a suspected pandemic infection. However, a patient's radiological findings may not be identified initially. Therefore, we have included an Artificial Intelligence (A.I.) algorithm-based Machine Intelligence (MI) system in this proposal to combine C.T. scan findings with all other tests, symptoms, and history to quickly diagnose a patient with a positive symptom of current and future pandemic diseases. Initially, the system will collect information by an infrared camera of the patient's facial regions to measure temperature, keep it as a record, and complete further actions. We divided the face into eight classes and twelve regions for temperature measurement. A database named patient-info-mask is maintained. While collecting sample data, we incorporate a wireless network using a cloudlets server to make processing more accessible with minimal infrastructure. The system will use deep learning approaches. We propose convolution neural networks (CNN) to cross-verify the collected data. For better results, we incorporated tenfold cross-verification into the synthesis method. As a result, our new way of estimating became more accurate and efficient. We achieved 3.29% greater accuracy by incorporating the "decision tree level synthesis method" and "ten-folded-validation method". It proves the robustness of our proposed method.

Mathematical biosciences and engineering : MBE

10.3934/mbe.2023050

Automated grading of chest x-ray images for viral pneumonia with convolutional neural networks ensemble and region of interest localization.

Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don't show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection. The CXR image analysis can also reveal additional disorders, such as pneumonia, which show up as anomalies in the lungs. Thus these CXRs can be used for automated grading aiding the doctors in making a better diagnosis. In order to classify a CXR image into the Negative for Pneumonia, Typical, Indeterminate, and Atypical, we used the publicly available CXR image competition dataset SIIM-FISABIO-RSNA COVID-19 from Kaggle. The suggested architecture employed an ensemble of EfficientNetv2-L for classification, which was trained via transfer learning from the initialised weights of ImageNet21K on various subsets of data (Code for the proposed methodology is available at: https://github.com/asadkhan1221/siim-covid19.git). To identify and localise opacities, an ensemble of YOLO was combined using Weighted Boxes Fusion (WBF). Significant generalisability gains were made possible by the suggested technique's addition of classification auxiliary heads to the CNN backbone. The suggested method improved further by utilising test time augmentation for both classifiers and localizers. The results for Mean Average Precision score show that the proposed deep learning model achieves 0.617 and 0.609 on public and private sets respectively and these are comparable to other techniques for the Kaggle dataset.

PloS one

10.1371/journal.pone.0280352 10.1007/s13246-020-00865-4 10.1186/s40537-020-00392-9 10.1016/j.compbiomed.2021.104771 10.1038/s41598-020-76550-z 10.1007/s10489-020-01888-w 10.1111/exsy.12759 10.1007/s10462-020-09825-6 10.1007/s13244-018-0639-9 10.1016/j.bspc.2021.102764 10.1016/j.asoc.2020.106691 10.1016/j.compbiomed.2020.103792 10.1007/s13755-021-00146-8 10.1007/s00521-020-05636-6 10.1109/ICCV.2017.74 10.1016/j.compbiomed.2021.104356 10.1016/j.compbiomed.2021.104306 10.1016/j.compbiomed.2021.104304 10.1007/978-3-030-88163-4_33 10.1016/j.neucom.2020.07.144 10.1007/s10044-021-00984-y 10.1080/0952813X.2021.1908431 10.1016/j.bspc.2022.103677 10.1080/07391102.2020.1767212 10.1016/j.compbiomed.2021.104348 10.1016/j.compbiomed.2022.105604 10.1016/j.compbiomed.2021.104375 10.1002/ima.22627 10.3390/app11062884 10.1016/j.ijmedinf.2020.104284 10.1016/j.eswa.2020.114054 10.1049/ipr2.12153 10.1016/j.compbiomed.2021.105002 10.1016/j.media.2021.102299 10.1155/2021/8890226 10.1016/j.media.2021.102046 10.1136/bmjqs-2018-008370

Carotid Vessel-Wall-Volume Ultrasound Measurement via a UNet++ Ensemble Algorithm Trained on Small Data Sets.

Vessel wall volume (VWV) is a 3-D ultrasound measurement for the assessment of therapy in patients with carotid atherosclerosis. Deep learning can be used to segment the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) and to quantify VWV automatically; however, it typically requires large training data sets with expert manual segmentation, which are difficult to obtain. In this study, a UNet++ ensemble approach was developed for automated VWV measurement, trained on five small data sets (n = 30 participants) and tested on 100 participants with clinically diagnosed coronary artery disease enrolled in a multicenter CAIN trial. The Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), Pearson correlation coefficient (r), Bland-Altman plots and coefficient of variation (CoV) were used to evaluate algorithm segmentation accuracy, agreement and reproducibility. The UNet++ ensemble yielded DSCs of 91.07%-91.56% and 87.53%-89.44% and ASSDs of 0.10-0.11 mm and 0.33-0.39 mm for the MAB and LIB, respectively; the algorithm VWV measurements were correlated (r = 0.763-0.795, p < 0.001) with manual segmentations, and the CoV for VWV was 8.89%. In addition, the UNet++ ensemble trained on 30 participants achieved a performance similar to that of U-Net and Voxel-FCN trained on 150 participants. These results suggest that our approach could provide accurate and reproducible carotid VWV measurements using relatively small training data sets, supporting deep learning applications for monitoring atherosclerosis progression in research and clinical trials.

Ultrasound in medicine & biology

10.1016/j.ultrasmedbio.2022.12.005

ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans.

Automated diagnostic techniques based on computed tomography (CT) scans of the chest for the coronavirus disease (COVID-19) help physicians detect suspected cases rapidly and precisely, which is critical in providing timely medical treatment and preventing the spread of epidemic outbreaks. Existing capsule networks have played a significant role in automatic COVID-19 detection systems based on small datasets. However, extracting key slices is difficult because CT scans typically show many scattered lesion sections. In addition, existing max pooling sampling methods cannot effectively fuse the features from multiple regions. Therefore, in this study, we propose an attention capsule sampling network (ACSN) to detect COVID-19 based on chest CT scans. A key slices enhancement method is used to obtain critical information from a large number of slices by applying attention enhancement to key slices. Then, the lost active and background features are retained by integrating two types of sampling. The results of experiments on an open dataset of 35,000 slices show that the proposed ACSN achieve high performance compared with state-of-the-art models and exhibits 96.3% accuracy, 98.8% sensitivity, 93.8% specificity, and 98.3% area under the receiver operating characteristic curve.

Computers in biology and medicine

10.1016/j.compbiomed.2022.106338 10.21203/rs.3.rs-32511/v1 10.1109/ACCESS.2021.3067311 10.3390/s22176709 10.1109/TII.2021.3056386 10.1109/CVPR.2016.90 10.48550/arXiv.2010.16041

Utilisation of deep learning for COVID-19 diagnosis.

The COVID-19 pandemic that began in 2019 has resulted in millions of deaths worldwide. Over this period, the economic and healthcare consequences of COVID-19 infection in survivors of acute COVID-19 infection have become apparent. During the course of the pandemic, computer analysis of medical images and data have been widely used by the medical research community. In particular, deep-learning methods, which are artificial intelligence (AI)-based approaches, have been frequently employed. This paper provides a review of deep-learning-based AI techniques for COVID-19 diagnosis using chest radiography and computed tomography. Thirty papers published from February 2020 to March 2022 that used two-dimensional (2D)/three-dimensional (3D) deep convolutional neural networks combined with transfer learning for COVID-19 detection were reviewed. The review describes how deep-learning methods detect COVID-19, and several limitations of the proposed methods are highlighted.

Clinical radiology

10.1016/j.crad.2022.11.006 10.1101/2020.02.11.20021493 10.5555/3305890.3305954 10.1109/ICCVW.2019.00052

Coronavirus covid-19 detection by means of explainable deep learning.

The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical management of patients with the coronavirus disease has undergone an evolution over the months, thanks to the increasing knowledge of the virus, symptoms and efficacy of the various therapies. Currently, however, there is no specific therapy for SARS-CoV-2 virus, know also as Coronavirus disease 19, and treatment is based on the symptoms of the patient taking into account the overall clinical picture. Furthermore, the test to identify whether a patient is affected by the virus is generally performed on sputum and the result is generally available within a few hours or days. Researches previously found that the biomedical imaging analysis is able to show signs of pneumonia. For this reason in this paper, with the aim of providing a fully automatic and faster diagnosis, we design and implement a method adopting deep learning for the novel coronavirus disease detection, starting from computed tomography medical images. The proposed approach is aimed to detect whether a computed tomography medical images is related to an healthy patient, to a patient with a pulmonary disease or to a patient affected with Coronavirus disease 19. In case the patient is marked by the proposed method as affected by the Coronavirus disease 19, the areas symptomatic of the Coronavirus disease 19 infection are automatically highlighted in the computed tomography medical images. We perform an experimental analysis to empirically demonstrate the effectiveness of the proposed approach, by considering medical images belonging from different institutions, with an average time for Coronavirus disease 19 detection of approximately 8.9 s and an accuracy equal to 0.95.

Scientific reports

10.1038/s41598-023-27697-y 10.1016/j.procs.2020.09.258 10.1038/s41577-020-00434-6 10.1016/j.cmpb.2020.105608 10.1038/d41573-020-00073-5 10.1056/NEJMoa2001316 10.1053/j.gastro.2020.02.054 10.3390/biology9050097 10.1002/jmv.25748 10.1056/NEJMp030078 10.1038/nm1024 10.3201/eid2009.140378 10.3390/jcm9020523 10.1001/jama.2020.4344 10.1016/S0140-6736(20)30185-9 10.1016/S0140-6736(20)30183-5 10.1148/radiol.2020200905 10.1016/j.ejrad.2020.108961 10.1111/irv.12734 10.1148/radiol.2020200432 10.1016/S1470-2045(19)30739-9 10.1097/RLI.0b013e318074fd81 10.2967/jnumed.117.189704 10.1007/s00330-007-0613-2 10.1007/978-3-031-01821-3 10.1109/5.726791 10.1038/s41598-020-76282-0 10.1007/s00330-021-07715-1 10.1016/j.eng.2020.04.010 10.1007/s13246-020-00865-4 10.1109/TCBB.2021.3065361 10.1016/j.compbiomed.2020.103792 10.1016/j.compbiomed.2020.103795 10.1016/j.bbe.2015.12.005 10.1016/j.measurement.2020.108116 10.1016/j.ifacol.2021.10.282 10.1016/j.cose.2021.102198 10.1109/ACCESS.2019.2961754

LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases.

This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and dropout rate, were tuned for the model. Next, three datasets of lung diseases were formed from separate open-access sources. One was a CT scan dataset containing 1043 images. Two X-ray datasets comprising images of COVID-19-affected lungs, pneumonia-affected lungs, and healthy lungs exist, with one being an imbalanced dataset with 5935 images and the other being a balanced dataset with 5002 images. The performance of each model was analyzed using the Adam, Nadam, and SGD optimizers. The best results have been obtained for both the CT scan and CXR datasets using the Nadam optimizer. For the CT scan images, LDDNet showed a COVID-19-positive classification accuracy of 99.36%, a 100% precision recall of 98%, and an F1 score of 99%. For the X-ray dataset of 5935 images, LDDNet provides a 99.55% accuracy, 73% recall, 100% precision, and 85% F1 score using the Nadam optimizer in detecting COVID-19-affected patients. For the balanced X-ray dataset, LDDNet provides a 97.07% classification accuracy. For a given set of parameters, the performance results of LDDNet are better than the existing algorithms of ResNet152V2 and XceptionNet.

Sensors (Basel, Switzerland)

10.3390/s23010480 10.1016/j.scitotenv.2020.138762 10.1007/s10489-020-01826-w 10.1101/2020.04.22.056283 10.1007/s10489-021-02393-4 10.1016/j.bea.2021.100003 10.1148/radiol.2020200527 10.1148/radiol.2020200823 10.1016/j.ejrad.2020.108961 10.1155/2021/5527923 10.1016/j.compbiomed.2020.103795 10.3390/s21020369 10.1016/j.cmpb.2019.06.005 10.1109/RBME.2020.2990959 10.1007/s42979-021-00785-4 10.1007/s10489-020-01943-6 10.1016/j.compbiomed.2021.104575 10.3233/HIS-210008 10.1101/2020.04.24.20078584 10.2174/1573405617666210713113439 10.1016/j.bspc.2021.102588 10.1109/ACCESS.2020.3010287 10.20944/preprints202003.0300.v1 10.1007/s10140-020-01886-y 10.1101/2020.03.12.20027185 10.1007/s00330-021-07715-1 10.1371/journal.pone.0259179 10.1016/j.compbiomed.2022.105213 10.3390/s22020669 10.1016/j.compbiomed.2022.105418 10.3390/info11090419 10.1038/s41597-021-00900-3 10.5281/zenodo.3757476 10.3390/app10217639 10.1016/j.patrec.2021.08.035 10.1109/JBHI.2022.3177854

A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network.

Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs, computed tomography (CT) scan, and electrocardiogram (ECG) trace images are the most widely known for early discovery and analysis of the coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i.e., binary, three-class, and four-class classifications. In addition, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i.e., binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 Radiography Database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms state-of-the-art DL approaches, such as Squeezenet, Alexnet, and Darknet19, by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.

Diagnostics (Basel, Switzerland)

10.3390/diagnostics13010162 10.3390/app12126269 10.1038/s41368-020-0075-9 10.3389/fmed.2022.1005920 10.1016/j.radi.2020.10.013 10.1016/j.patcog.2021.108255 10.1007/s00521-021-06737-6 10.1016/j.compbiomed.2022.105350 10.1016/j.cmpb.2022.106731 10.1101/2020.03.30.20047787 10.1016/j.cma.2022.114570 10.1016/j.cma.2020.113609 10.1016/j.cie.2021.107250 10.1016/j.eswa.2021.116158 10.1109/ACCESS.2022.3147821 10.1007/s13246-020-00888-x 10.1016/j.compbiomed.2020.103792 10.1016/j.cmpb.2020.105581 10.1007/s40846-020-00529-4 10.1016/j.mehy.2020.109761 10.18576/amis/100122 10.12785/amis/080617 10.1016/j.jksuci.2021.12.017 10.1155/2012/205391 10.1038/s41591-020-0931-3 10.1007/s00330-021-07715-1 10.1016/j.patcog.2021.108135 10.3390/s21175702 10.1155/2021/3366057 10.1007/s13755-021-00169-1 10.1016/j.jrras.2022.02.002 10.1109/ACCESS.2020.3010287 10.1016/j.compbiomed.2021.104319 10.1101/2020.04.24.20078584 10.1016/j.dib.2021.106762 10.1145/3065386 10.3390/technologies10020037 10.1016/j.eswa.2021.116377 10.1155/2022/4130674 10.1155/2022/6486570 10.3390/app12115645 10.3390/s22197575 10.3390/electronics11071146 10.1109/ACCESS.2022.3189676 10.1109/ACCESS.2019.2909969 10.1109/ACCESS.2019.2904800 10.3390/s22051747 10.1007/s00521-022-08007-5 10.1007/s00500-022-07420-1 10.1007/s00521-021-06631-1

An Efficient Deep Learning Method for Detection of COVID-19 Infection Using Chest X-ray Images.

The research community has recently shown significant interest in designing automated systems to detect coronavirus disease 2019 (COVID-19) using deep learning approaches and chest radiography images. However, state-of-the-art deep learning techniques, especially convolutional neural networks (CNNs), demand more learnable parameters and memory. Therefore, they may not be suitable for real-time diagnosis. Thus, the design of a lightweight CNN model for fast and accurate COVID-19 detection is an urgent need. In this paper, a lightweight CNN model called LW-CORONet is proposed that comprises a sequence of convolution, rectified linear unit (ReLU), and pooling layers followed by two fully connected layers. The proposed model facilitates extracting meaningful features from the chest X-ray (CXR) images with only five learnable layers. The proposed model is evaluated using two larger CXR datasets (Dataset-1: 2250 images and Dataset-2: 15,999 images) and the classification accuracy obtained are 98.67% and 99.00% on Dataset-1 and 95.67% and 96.25% on Dataset-2 for multi-class and binary classification cases, respectively. The results are compared with four contemporary pre-trained CNN models as well as state-of-the-art models. The effect of several hyperparameters: different optimization techniques, batch size, and learning rate have also been investigated. The proposed model demands fewer parameters and requires less memory space. Hence, it is effective for COVID-19 detection and can be utilized as a supplementary tool to assist radiologists in their diagnosis.

Diagnostics (Basel, Switzerland)

10.3390/diagnostics13010131 10.1016/S0140-6736(20)30183-5 10.32604/cmc.2020.010691 10.1148/radiol.2020200432 10.1109/RBME.2020.2990959 10.1148/radiol.2020200527 10.1148/radiol.2020200230 10.1109/JBHI.2022.3196489 10.1148/radiol.2020200343 10.1109/RBME.2020.2987975 10.3390/app10020559 10.1148/radiol.2017162326 10.1371/journal.pmed.1002686 10.1016/j.compbiomed.2020.103792 10.1007/s10044-021-00984-y 10.1016/j.mehy.2020.109761 10.1016/j.imu.2020.100360 10.1038/s41598-020-76550-z 10.1016/j.compbiomed.2020.103805 10.1016/j.chaos.2020.110122 10.1109/TMI.2020.2996256 10.1109/JSEN.2020.3025855 10.1016/j.inffus.2020.11.005 10.1016/j.compbiomed.2021.104454 10.1145/3551647 10.1016/j.bspc.2021.103182 10.1016/j.compbiomed.2022.106331 10.1016/j.bspc.2020.102365 10.1007/s11042-022-12156-z 10.1109/ACCESS.2019.2950228 10.1007/BF03178082 10.1016/j.neucom.2017.12.030 10.1109/TMI.2016.2528162 10.1186/s40537-019-0197-0 10.1016/j.patrec.2020.04.018 10.1007/s12652-020-02612-9 10.1016/j.compmedimag.2019.05.001 10.1016/j.media.2017.07.005

Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms.

One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID-19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch. In the deep learning model for the diagnosis, we proposed a transformer model that learns HR variability patterns in presymptom by tracking relationships in sequential HR data. In the cross-validation (CV) results from the COVID-19 unvaccinated patients, our proposed deep learning model exhibited high accuracy metrics: sensitivity of 84.38%, specificity of 85.25%, accuracy of 84.85%, balanced accuracy of 84.81%, and area under the receiver operating characteristics (AUROC) of 0.8778. Furthermore, we validated our model using external multiple datasets including healthy subjects, COVID-19 patients, as well as vaccinated patients. In the external healthy subject group, our model also achieved high specificity of 77.80%. In the external COVID-19 unvaccinated patient group, our model also provided similar accuracy metrics to those from the CV: balanced accuracy of 87.23% and AUROC of 0.8897. In the COVID-19 vaccinated patients, the balanced accuracy and AUROC dropped by 66.67% and 0.8072, respectively. The first finding in this study is that our proposed deep learning model can simply and accurately diagnose COVID-19 patients using HRs obtained from a smartwatch before the onset of symptoms. The second finding is that the model trained from unvaccinated patients may provide less accurate diagnosis performance compared with the vaccinated patients. The last finding is that the model trained in a certain period of time may provide degraded diagnosis performances as the virus continues to mutate.

Journal of medical virology

10.1002/jmv.28462

Identification of Asymptomatic COVID-19 Patients on Chest CT Images Using Transformer-Based or Convolutional Neural Network-Based Deep Learning Models.

Novel coronavirus disease 2019 (COVID-19) has rapidly spread throughout the world; however, it is difficult for clinicians to make early diagnoses. This study is to evaluate the feasibility of using deep learning (DL) models to identify asymptomatic COVID-19 patients based on chest CT images. In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs) or transformer architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou were randomly split into a training set (n = 2140) and an internal-validation set (n = 360). Data from Suzhou was the external-test set (n = 200). Model performance was assessed by the metrics accuracy, recall, and specificity and was compared with the assessments of two radiologists. A total of 2700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by the EfficientNet model (0.954). The recall and the precision of the Swin model were 0.989 and 1.000, respectively. In the test dataset, the Swin model was still the best and achieved the highest accuracy (0.980). All the DL models performed remarkably better than the two experts. Last, the time on the test set diagnosis spent by two experts-42 min, 17 s (junior); and 29 min, 43 s (senior)-was significantly higher than those of the DL models (all below 2 min). This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found that a transformer-based model, the Swin model, performed best.

Journal of digital imaging

10.1007/s10278-022-00754-0 10.1056/NEJMoa2002032 10.1007/s00330-020-06886-7 10.1186/s12911-021-01521-x 10.1016/j.compbiomed.2020.103805 10.1016/s0140-6736(20)30211-7 10.1001/jama.2020.1585 10.1148/radiol.2020200642 10.1148/radiol.2020200230 10.1016/s0140-6736(20)30183-5 10.1148/radiol.2020200463 10.1016/j.chest.2020.04.003 10.1001/jama.2020.12839 10.1038/s41598-020-74164-z 10.1148/radiol.2020200823 10.1016/s1473-3099(14)70846-1 10.1001/jama.2020.2565 10.1016/j.ijid.2020.06.052 10.1038/s41467-020-17971-2 10.1007/s00330-020-07042-x 10.1109/tmi.2020.3040950 10.1016/j.cell.2020.04.045 10.1016/j.compbiomed.2020.103792 10.1038/s41598-020-76550-z 10.3389/fimmu.2021.732756 10.1002/smll.202002169 10.1016/j.talanta.2020.121726 10.1148/radiol.2020200490 10.1148/radiol.2020201365 10.1016/j.annonc.2020.04.003 10.1016/j.ejrad.2020.108961 10.1148/radiol.2020200343 10.1126/science.abb3221 10.1007/s00330-021-07715-1 10.1109/rbme.2020.2987975 10.1155/2021/5185938 10.1148/radiol.2020201491 10.1109/tmi.2016.2528162

Covid-19 Diagnosis by WE-SAJ.

With a global COVID-19 pandemic, the number of confirmed patients increases rapidly, leaving the world with very few medical resources. Therefore, the fast diagnosis and monitoring of COVID-19 are one of the world's most critical challenges today. Artificial intelligence-based CT image classification models can quickly and accurately distinguish infected patients from healthy populations. Our research proposes a deep learning model (WE-SAJ) using wavelet entropy for feature extraction, two-layer FNNs for classification and the adaptive Jaya algorithm as a training algorithm. It achieves superior performance compared to the Jaya-based model. The model has a sensitivity of 85.47±1.84, specificity of 87.23±1.67 precision of 87.03±1.34, an accuracy of 86.35±0.70, and F1 score of 86.23±0.77, Matthews correlation coefficient of 72.75±1.38, and Fowlkes-Mallows Index of 86.24±0.76. Our experiments demonstrate the potential of artificial intelligence techniques for COVID-19 diagnosis and the effectiveness of the Self-adaptive Jaya algorithm compared to the Jaya algorithm for medical image classification tasks.

Systems science & control engineering

10.1080/21642583.2022.2045645

A Review of COVID-19 Diagnostic Approaches in Computer Vision.

Computer vision has proven that it can solve many problems in the field of health in recent years. Processing the data obtained from the patients provided benefits in both disease detection and follow-up and control mechanisms. Studies on the use of computer vision for COVID-19, which is one of the biggest global health problems of the past years, are increasing daily. This study includes a preliminary review of COVID-19 computer vision research conducted in recent years. This review aims to help researchers who want to work in this field.

Current medical imaging

10.2174/1573405619666221222161832

Detection of COVID-19 in X-ray Images Using Densely Connected Squeeze Convolutional Neural Network (DCSCNN): Focusing on Interpretability and Explainability of the Black Box Model.

The novel coronavirus (COVID-19), which emerged as a pandemic, has engulfed so many lives and affected millions of people across the world since December 2019. Although this disease is under control nowadays, yet it is still affecting people in many countries. The traditional way of diagnosis is time taking, less efficient, and has a low rate of detection of this disease. Therefore, there is a need for an automatic system that expedites the diagnosis process while retaining its performance and accuracy. Artificial intelligence (AI) technologies such as machine learning (ML) and deep learning (DL) potentially provide powerful solutions to address this problem. In this study, a state-of-the-art CNN model densely connected squeeze convolutional neural network (DCSCNN) has been developed for the classification of X-ray images of COVID-19, pneumonia, normal, and lung opacity patients. Data were collected from different sources. We applied different preprocessing techniques to enhance the quality of images so that our model could learn accurately and give optimal performance. Moreover, the attention regions and decisions of the AI model were visualized using the Grad-CAM and LIME methods. The DCSCNN combines the strength of the Dense and Squeeze networks. In our experiment, seven kinds of classification have been performed, in which six are binary classifications (COVID vs. normal, COVID vs. lung opacity, lung opacity vs. normal, COVID vs. pneumonia, pneumonia vs. lung opacity, pneumonia vs. normal) and one is multiclass classification (COVID vs. pneumonia vs. lung opacity vs. normal). The main contributions of this paper are as follows. First, the development of the DCSNN model which is capable of performing binary classification as well as multiclass classification with excellent classification accuracy. Second, to ensure trust, transparency, and explainability of the model, we applied two popular Explainable AI techniques (XAI). i.e., Grad-CAM and LIME. These techniques helped to address the black-box nature of the model while improving the trust, transparency, and explainability of the model. Our proposed DCSCNN model achieved an accuracy of 98.8% for the classification of COVID-19 vs normal, followed by COVID-19 vs. lung opacity: 98.2%, lung opacity vs. normal: 97.2%, COVID-19 vs. pneumonia: 96.4%, pneumonia vs. lung opacity: 95.8%, pneumonia vs. normal: 97.4%, and lastly for multiclass classification of all the four classes i.e., COVID vs. pneumonia vs. lung opacity vs. normal: 94.7%, respectively. The DCSCNN model provides excellent classification performance consequently, helping doctors to diagnose diseases quickly and efficiently.

Sensors (Basel, Switzerland)

10.3390/s22249983 10.1016/S0140-6736(66)92364-6 10.1016/j.celrep.2020.108175 10.1159/000149390 10.1001/jama.2020.1097 10.1016/S0140-6736(20)30251-8 10.1016/S0140-6736(21)02758-6 10.15585/mmwr.mm7050e1 10.2174/0929867328666210521164809 10.1111/cbdd.13761 10.1002/ped4.12178 10.1093/cid/ciaa799 10.1148/radiol.2020200642 10.1001/jama.2020.3786 10.3390/diagnostics10030165 10.1001/jama.2020.1585 10.1148/radiol.2020200432 10.1016/j.measurement.2019.05.076 10.54112/bcsrj.v2020i1.31 10.1007/s12195-020-00629-w 10.1007/s10044-021-00984-y 10.1007/s00330-021-07715-1 10.20944/preprints202003.0300.v1 10.1016/j.chaos.2020.110120 10.1007/s13246-020-00865-4 10.1016/j.compbiomed.2020.104037 10.1016/j.bbe.2020.08.008 10.3390/life11101092 10.3390/diagnostics11050829 10.3390/jpm12060988 10.1109/JBHI.2022.3168604 10.32604/cmc.2020.013249 10.1016/j.inffus.2021.07.016 10.3892/etm.2020.8797 10.1016/j.compbiomed.2022.105244 10.1002/ima.22706 10.1038/s41598-020-76550-z 10.1109/TMI.2020.2993291 10.1016/j.compbiomed.2021.104335 10.1016/j.intimp.2020.106705 10.1007/s10489-020-01829-7 10.1007/s42600-020-00112-5 10.1016/j.mri.2014.03.010 10.1109/42.996338 10.1016/j.chaos.2020.110190 10.1016/j.compbiomed.2020.104041

Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust.

Due to the severity and speed of spread of the ongoing Covid-19 pandemic, fast but accurate diagnosis of Covid-19 patients has become a crucial task. Achievements in this respect might enlighten future efforts for the containment of other possible pandemics. Researchers from various fields have been trying to provide novel ideas for models or systems to identify Covid-19 patients from different medical and non-medical data. AI-based researchers have also been trying to contribute to this area by mostly providing novel approaches of automated systems using convolutional neural network (CNN) and deep neural network (DNN) for Covid-19 detection and diagnosis. Due to the efficiency of deep learning (DL) and transfer learning (TL) models in classification and segmentation tasks, most of the recent AI-based researches proposed various DL and TL models for Covid-19 detection and infected region segmentation from chest medical images like X-rays or CT images. This paper describes a web-based application framework for Covid-19 lung infection detection and segmentation. The proposed framework is characterized by a feedback mechanism for self learning and tuning. It uses variations of three popular DL models, namely Mask R-CNN, U-Net, and U-Net++. The models were trained, evaluated and tested using CT images of Covid patients which were collected from two different sources. The web application provide a simple user friendly interface to process the CT images from various resources using the chosen models, thresholds and other parameters to generate the decisions on detection and segmentation. The models achieve high performance scores for Dice similarity, Jaccard similarity, accuracy, loss, and precision values. The U-Net model outperformed the other models with more than 98% accuracy.

PloS one

10.1371/journal.pone.0278487 10.3390/v12040372 10.1080/14737159.2020.1757437 10.1148/radiol.2020203173 10.3389/fpubh.2022.1046296 10.1148/rg.2020200159 10.1155/2021/2560388 10.32604/cmc.2023.032064 10.3390/s21062215 10.3390/ijerph18031117 10.1016/j.compbiomed.2022.105350 10.3389/fcvm.2021.638011 10.1155/2020/9756518 10.1109/ACCESS.2021.3054484 10.1016/j.dsx.2020.05.008 10.1109/RBME.2020.2987975 10.1016/j.jiph.2020.06.028 10.3390/jcm10091961 10.1007/s10489-020-02102-7 10.1016/j.chaos.2020.110059 10.3389/fpubh.2022.948205 10.1016/j.neucom.2021.03.034 10.1016/j.bea.2022.100041 10.1007/s13369-021-05958-0 10.1016/j.asoc.2021.107522 10.1016/j.patcog.2020.107747 10.1038/s41598-022-06854-9 10.1007/s10489-021-02731-6 10.3390/electronics11152296 10.1007/s11063-022-10785-x 10.1007/s13755-021-00146-8 10.1038/s41598-020-76282-0 10.1016/j.cmpbup.2021.100007 10.1002/mp.15231 10.3390/electronics11010130 10.7717/peerj-cs.349 10.1016/j.patcog.2021.107828 10.3390/su13031224 10.1016/j.cell.2020.04.045 10.3389/fmed.2020.608525 10.1016/j.compbiomed.2021.104319 10.1186/s41747-020-00173-2 10.5194/isprs-archives-XLIII-B3-2020-1507-2020 10.1021/acs.jcim.8b00671 10.1145/3352020.3352029 10.3390/brainsci10070427 10.3390/app10051897 10.1016/j.neunet.2015.07.007 10.1016/j.neucom.2022.01.014 10.1016/j.media.2021.102035 10.1186/s12880-015-0068-x

Disease Recognition in X-ray Images with Doctor Consultation-Inspired Model.

The application of chest X-ray imaging for early disease screening is attracting interest from the computer vision and deep learning community. To date, various deep learning models have been applied in X-ray image analysis. However, models perform inconsistently depending on the dataset. In this paper, we consider each individual model as a medical doctor. We then propose a doctor consultation-inspired method that fuses multiple models. In particular, we consider both early and late fusion mechanisms for consultation. The early fusion mechanism combines the deep learned features from multiple models, whereas the late fusion method combines the confidence scores of all individual models. Experiments on two X-ray imaging datasets demonstrate the superiority of the proposed method relative to baseline. The experimental results also show that early consultation consistently outperforms the late consultation mechanism in both benchmark datasets. In particular, the early doctor consultation-inspired model outperforms all individual models by a large margin, i.e., 3.03 and 1.86 in terms of accuracy in the UIT COVID-19 and chest X-ray datasets, respectively.

Journal of imaging

10.3390/jimaging8120323 10.1148/radiol.2020200642 10.1016/j.cca.2020.03.009 10.1145/3065386 10.7861/futurehosp.6-2-94 10.3390/info13080360 10.1038/s41551-018-0305-z 10.1016/j.jacr.2019.05.047 10.1016/j.imu.2020.100405 10.20944/preprints202003.0300.v1 10.1016/j.bbe.2020.08.005 10.1038/s41591-020-0931-3 10.1097/RTI.0000000000000512 10.1016/j.chemolab.2020.104054 10.1109/TPAMI.2007.1110 10.1016/j.compbiomed.2020.103795 10.1109/TPAMI.2020.2983686 10.1109/ACCESS.2020.2994762 10.1109/TMI.2020.2993291 10.3390/s21217116 10.3390/ijerph17186933 10.1016/j.artmed.2021.102156 10.1016/j.compbiomed.2022.105383 10.1007/s10489-020-01831-z 10.3390/jimaging7020012 10.1109/TPAMI.2017.2723009

Improving COVID-19 CT classification of CNNs by learning parameter-efficient representation.

The COVID-19 pandemic continues to spread rapidly over the world and causes a tremendous crisis in global human health and the economy. Its early detection and diagnosis are crucial for controlling the further spread. Many deep learning-based methods have been proposed to assist clinicians in automatic COVID-19 diagnosis based on computed tomography imaging. However, challenges still remain, including low data diversity in existing datasets, and unsatisfied detection resulting from insufficient accuracy and sensitivity of deep learning models. To enhance the data diversity, we design augmentation techniques of incremental levels and apply them to the largest open-access benchmark dataset, COVIDx CT-2A. Meanwhile, similarity regularization (SR) derived from contrastive learning is proposed in this study to enable CNNs to learn more parameter-efficient representations, thus improve the accuracy and sensitivity of CNNs. The results on seven commonly used CNNs demonstrate that CNN performance can be improved stably through applying the designed augmentation and SR techniques. In particular, DenseNet121 with SR achieves an average test accuracy of 99.44% in three trials for three-category classification, including normal, non-COVID-19 pneumonia, and COVID-19 pneumonia. The achieved precision, sensitivity, and specificity for the COVID-19 pneumonia category are 98.40%, 99.59%, and 99.50%, respectively. These statistics suggest that our method has surpassed the existing state-of-the-art methods on the COVIDx CT-2A dataset. Source code is available at https://github.com/YujiaKCL/COVID-CT-Similarity-Regularization.

Computers in biology and medicine

10.1016/j.compbiomed.2022.106417 10.1136/bmj.n597 10.3389/fmed.2020.608525 10.5281/zenodo.4414861

Virus Detection and Identification in Minutes Using Single-Particle Imaging and Deep Learning.

The increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive diagnostic methods. Here, we present a methodology for virus detection and identification that uses a convolutional neural network to distinguish between microscopy images of fluorescently labeled intact particles of different viruses. Our assay achieves labeling, imaging, and virus identification in less than 5 min and does not require any lysis, purification, or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses. We were also able to differentiate closely related strains of influenza, as well as SARS-CoV-2 variants. Additional and novel pathogens can easily be incorporated into the test through software updates, offering the potential to rapidly utilize the technology in future infectious disease outbreaks or pandemics. Single-particle imaging combined with deep learning therefore offers a promising alternative to traditional viral diagnostic and genomic sequencing methods and has the potential for significant impact.

ACS nano

10.1021/acsnano.2c10159 10.1021/acsnano.0c02624 10.1111/1751-7915.13586 10.1007/s12250-020-00218-1 10.1371/journal.pone.0234682 10.1101/2020.02.26.20028373 10.1039/D0AN01835J 10.1021/acscentsci.0c01288 10.1002/14651858.CD013705.pub2 10.1038/s41598-019-52759-5 10.1162/neco_a_00990 10.1038/nature14539 10.7554/eLife.40183 10.1007/s12560-018-9335-7 10.1093/cid/ciaa1382 10.1016/S1473-3099(20)30113-4 10.7150/ijbs.45018 10.1111/j.1365-2672.2010.04663.x 10.1002/elps.202000121 10.1016/0166-0934(91)90012-O 10.1038/s41598-021-91371-4 10.1128/JVI.75.24.12359-12369.2001 10.2807/1560-7917.ES.2021.26.3.2100008 10.1136/bmj.308.6943.1552

Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis.

Chest X-ray (CXR) imaging is a low-cost, easy-to-use imaging alternative that can be used to diagnose/screen pulmonary abnormalities due to infectious diseaseX: Covid-19, Pneumonia and Tuberculosis (TB). Not limited to binary decisions (with respect to healthy cases) that are reported in the state-of-the-art literature, we also consider non-healthy CXR screening using a lightweight deep neural network (DNN) with a reduced number of epochs and parameters. On three diverse publicly accessible and fully categorized datasets, for non-healthy versus healthy CXR screening, the proposed DNN produced the following accuracies: 99.87% on Covid-19 versus healthy, 99.55% on Pneumonia versus healthy, and 99.76% on TB versus healthy datasets. On the other hand, when considering non-healthy CXR screening, we received the following accuracies: 98.89% on Covid-19 versus Pneumonia, 98.99% on Covid-19 versus TB, and 100% on Pneumonia versus TB. To further precisely analyze how well the proposed DNN worked, we considered well-known DNNs such as ResNet50, ResNet152V2, MobileNetV2, and InceptionV3. Our results are comparable with the current state-of-the-art, and as the proposed CNN is light, it could potentially be used for mass screening in resource-constraint regions.

Information sciences

10.1016/j.ins.2022.01.062

LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery.

Automatic detection of lung diseases using AI-based tools became very much necessary to handle the huge number of cases occurring across the globe and support the doctors. This paper proposed a novel deep learning architecture named LWSNet (Light Weight Stacking Network) to separate Covid-19, cold pneumonia, and normal chest x-ray images. This framework is based on single, double, triple, and quadruple stack mechanisms to address the above-mentioned tri-class problem. In this framework, a truncated version of standard deep learning models and a lightweight CNN model was considered to conviniently deploy in resource-constraint devices. An evaluation was conducted on three publicly available datasets alongwith their combination. We received 97.28%, 96.50%, 97.41%, and 98.54% highest classification accuracies using quadruple stack. On further investigation, we found, using LWSNet, the average accuracy got improved from individual model to quadruple model by 2.31%, 2.55%, 2.88%, and 2.26% on four respective datasets.

Multimedia tools and applications

10.1007/s11042-022-14247-3 10.1111/exsy.12749 10.1145/3431804 10.1148/radiol.2020200642 10.1007/s10489-021-02199-4 10.1007/s13246-020-00865-4 10.3390/ijerph19042013 10.7717/peerj-cs.551 10.1007/s11042-021-11103-8 10.1007/s00371-021-02094-6 10.1038/s41598-018-33214-3 10.1016/S0140-6736(20)30183-5 10.1016/j.imu.2020.100412 10.1007/s10489-020-01902-1 10.3390/su14116785 10.1016/j.compbiomed.2020.104181 10.1038/nature14539 10.1016/j.neucom.2016.12.038 10.1016/j.compbiomed.2022.105213 10.31661/jbpe.v0i0.2008-1153 10.1007/s10489-020-01943-6 10.1016/j.bbe.2021.06.011 10.1109/JBHI.2021.3051470 10.1109/TNNLS.2021.3054746 10.1016/j.compbiomed.2021.104319 10.1016/j.imu.2020.100505 10.1007/s11548-020-02286-w 10.1016/j.bspc.2021.102622 10.1142/S0218001421510046 10.1109/TII.2021.3057683 10.1007/s11042-021-11807-x 10.1049/iet-ipr.2020.1127 10.1016/S0893-6080(05)80023-1 10.1002/jmv.26741 10.1371/journal.pone.0236621

Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees.

COVID-19 raised the need for automatic medical diagnosis, to increase the physicians' efficiency in managing the pandemic. Among all the techniques for evaluating the status of the lungs of a patient with COVID-19, lung ultrasound (LUS) offers several advantages: portability, cost-effectiveness, safety. Several works approached the automatic detection of LUS imaging patterns related COVID-19 by using deep neural networks (DNNs). However, the decision processes based on DNNs are not fully explainable, which generally results in a lack of trust from physicians. This, in turn, slows down the adoption of such systems. In this work, we use two previously built DNNs as feature extractors at the frame level, and automatically synthesize, by means of an evolutionary algorithm, a decision tree (DT) that aggregates in an interpretable way the predictions made by the DNNs, returning the severity of the patients' conditions according to a LUS score of prognostic value. Our results show that our approach performs comparably or better than previously reported aggregation techniques based on an empiric combination of frame-level predictions made by DNNs. Furthermore, when we analyze the evolved DTs, we discover properties about the DNNs used as feature extractors. We make our data publicly available for further development and reproducibility.

Applied soft computing

10.1016/j.asoc.2022.109926 10.1002/jum.15284 10.1002/jum.15285 10.1148/radiol.2020200847 10.1016/j.ejro.2020.100231 10.1016/j.jamda.2020.05.050 10.4269/ajtmh.20-0280 10.1186/s13054-020-02876-9 10.1007/s00134-020-05996-6 10.1007/s00134-020-06058-7 10.1121/10.0002183 10.1016/j.ultrasmedbio.2020.07.018 10.1109/TUFFC.2020.3012289 10.1121/10.0001797 10.1121/10.0001797 10.1007/s40477-017-0244-7 10.1016/j.ultrasmedbio.2017.01.011 10.1186/s12931-020-01338-8 10.1109/TMI.2020.2994459 10.1109/TUFFC.2020.3005512 10.1016/j.media.2021.101975 10.1109/TMI.2021.3117246 10.1002/jum.15548 10.1007/BFb0055930 10.1007/BFb0055930

Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images.

COVID-19 is one of the most life-threatening and dangerous diseases caused by the novel Coronavirus, which has already afflicted a larger human community worldwide. This pandemic disease recovery is possible if detected in the early stage. We proposed an automated deep learning approach from Computed Tomography (CT) scan images to detect COVID-19 positive patients by following a four-phase paradigm for COVID-19 detection: preprocess the CT scan images; remove noise from test image by using anisotropic diffusion techniques; make a different segment for the preprocessed images; and train and test COVID-19 detection using Convolutional Neural Network (CNN) models. This study employed well-known pre-trained models, including AlexNet, ResNet50, VGG16 and VGG19 to evaluate experiments. 80% of images are used to train the network in the detection process, while the remaining 20% are used to test it. The result of the experiment evaluation confirmed that the VGG19 pre-trained CNN model achieved better accuracy (98.06%). We used 4861 real-life COVID-19 CT images for experiment purposes, including 3068 positive and 1793 negative images. These images were acquired from a hospital in Sao Paulo, Brazil and two other different data sources. Our proposed method revealed very high accuracy and, therefore, can be used as an assistant to help professionals detect COVID-19 patients accurately.

Scientific reports

10.1038/s41598-022-25539-x 10.1016/S0140-6736(20)30183-5 10.1056/NEJMoa2001017 10.1002/jmv.25743 10.1016/j.ijsu.2020.02.034 10.1016/S1473-3099(20)30120-1 10.3389/fpubh.2020.00154 10.1148/radiol.2020200432 10.1148/radiol.2020200370 10.1148/radiol.2020200463 10.1016/j.ejrad.2020.108961 10.1016/S1473-3099(20)30134-1 10.2214/AJR.20.22954 10.1038/nature21056 10.1016/j.patrec.2020.03.011 10.1016/j.compmedimag.2019.101673 10.1109/JSEN.2019.2959617 10.1016/j.ejrad.2020.109041 10.1371/journal.pone.0250952 10.1038/s41467-020-18685-1 10.1016/j.compbiomed.2020.103795 10.1038/s41598-021-83424-5 10.1109/TCBB.2021.3065361 10.3390/e22050517 10.1016/j.compbiomed.2020.104037 10.1109/TMI.2020.2995965 10.1148/radiol.2020200905 10.3390/bdcc3020027 10.1007/s12065-020-00550-1 10.1109/ICMA.2013.6618111 10.1148/radiol.2020200230 10.1148/radiol.2020200241 10.1016/j.eng.2020.04.010 10.1183/13993003.00775-2020 10.1016/j.imu.2020.100405 10.1088/1361-6560/abe838

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence. The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores. This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.

European radiology

10.1007/s00330-022-09335-9 10.1148/radiol.2020203173 10.1148/radiol.2020200432 10.1148/radiol.2020200642 10.1038/s41568-018-0016-5 10.1148/radiol.2020200905 10.1148/radiol.2020201491 10.7150/thno.46465 10.1007/s00330-020-07044-9 10.1038/s41591-020-0931-3 10.1038/s41467-020-17971-2 10.1183/13993003.00775-2020 10.1183/13993003.01104-2020 10.1038/s41467-020-18685-1 10.1016/j.media.2020.101860 10.1038/s41746-020-00369-1 10.1148/radiol.2015141579 10.1186/s12931-021-01670-7 10.1186/s40779-020-0233-6 10.1148/radiol.2020200370 10.1109/TPAMI.2016.2644615 10.1148/radiol.2020200823 10.1080/22221751.2020.1750307 10.1148/radiol.2020201473 10.1097/RTI.0000000000000524 10.1016/j.ejrad.2021.110002

Diagnosis of COVID-19 Disease in Chest CT-Scan Images Based on Combination of Low-Level Texture Analysis and MobileNetV2 Features.

Since two years ago, the COVID-19 virus has spread strongly in the world and has killed more than 6 million people directly and has affected the lives of more than 500 million people. Early diagnosis of the virus can help to break the chain of transmission and reduce the death rate. In most cases, the virus spreads in the infected person's chest. Therefore, the analysis of a chest CT scan is one of the most efficient methods for diagnosing a patient. Until now, various methods have been presented to diagnose COVID-19 disease in chest CT-scan images. Most recent studies have proposed deep learning-based methods. But handcrafted features provide acceptable results in some studies too. In this paper, an innovative approach is proposed based on the combination of low-level and deep features. First of all, local neighborhood difference patterns are performed to extract handcrafted texture features. Next, deep features are extracted using MobileNetV2. Finally, a two-level decision-making algorithm is performed to improve the detection rate especially when the proposed decisions based on the two different feature set are not the same. The proposed approach is evaluated on a collected dataset of chest CT scan images from June 1, 2021, to December 20, 2021, of 238 cases in two groups of patient and healthy in different COVID-19 variants. The results show that the combination of texture and deep features can provide better performance than using each feature set separately. Results demonstrate that the proposed approach provides higher accuracy in comparison with some state-of-the-art methods in this scope.

Computational intelligence and neuroscience

10.1155/2022/1658615 10.1007/s10278-021-00445-2 10.2196/27468 10.1001/jama.2020.3786 10.1016/j.ejrad.2020.108961 10.1109/jbhi.2021.3074893 10.1148/radiol.2020200642 10.1148/radiol.2020200432 10.1109/rbme.2020.2987975 10.1007/s00330-021-07715-1 10.1148/radiol.2021203957 10.1007/s00330-021-08409-4 10.1155/2022/2564022 10.1101/2020.04.13.20063941 10.3390/s21020455 10.1016/j.cmpb.2020.105581 10.1007/s10140-020-01886-y 10.1007/s10044-021-00984-y 10.3390/app12104825 10.1016/j.cmpb.2020.105532 10.1016/j.ins.2020.09.041 10.3390/ijerph18063056 10.3390/healthcare9050522 10.3390/app11199023 10.3390/math10142472 10.3390/jpm12020309 10.1109/CVPR40276.2018 10.1007/3-540-45054-8_27 10.1007/s13369-013-0725-8 10.1109/tpami.2002.1017623 10.1109/tip.2010.2042645 10.1007/s11042-020-10321-w 10.1007/s11042-017-4834-3 10.11591/ijece.v11i1.pp844-850

Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images.

In this era of Coronavirus disease 2019 (COVID-19), an accurate method of diagnosis with less diagnosis time and cost can effectively help in controlling the disease spread with the new variants taking birth from time to time. In order to achieve this, a two-dimensional (2D) tunable Q-wavelet transform (TQWT) based on a memristive crossbar array (MCA) is introduced in this work for the decomposition of chest X-ray images of two different datasets. TQWT has resulted in promising values of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) at the optimum values of its parameters namely quality factor (Q) of 4, and oversampling rate (r) of 3 and at a decomposition level (J) of 2. The MCA-based model is used to process decomposed images for further classification with efficient storage. These images have been further used for the classification of COVID-19 and non-COVID-19 images using ResNet50 and AlexNet convolutional neural network (CNN) models. The average accuracy values achieved for the processed chest X-ray images classification in the small and large datasets are 98.82% and 94.64%, respectively which are higher than the reported conventional methods based on different models of deep learning techniques. The average accuracy of detection of COVID-19 via the proposed method of image classification has also been achieved with less complexity, energy, power, and area consumption along with lower cost estimation as compared to CMOS-based technology.

Computers in biology and medicine

10.1016/j.compbiomed.2022.106331 10.1007/s13755-020-00135-3

US-Net: A lightweight network for simultaneous speckle suppression and texture enhancement in ultrasound images.

Numerous traditional filtering approaches and deep learning-based methods have been proposed to improve the quality of ultrasound (US) image data. However, their results tend to suffer from over-smoothing and loss of texture and fine details. Moreover, they perform poorly on images with different degradation levels and mainly focus on speckle reduction, even though texture and fine detail enhancement are of crucial importance in clinical diagnosis. We propose an end-to-end framework termed US-Net for simultaneous speckle suppression and texture enhancement in US images. The architecture of US-Net is inspired by U-Net, whereby a feature refinement attention block (FRAB) is introduced to enable an effective learning of multi-level and multi-contextual representative features. Specifically, FRAB aims to emphasize high-frequency image information, which helps boost the restoration and preservation of fine-grained and textural details. Furthermore, our proposed US-Net is trained essentially with real US image data, whereby real US images embedded with simulated multi-level speckle noise are used as an auxiliary training set. Extensive quantitative and qualitative experiments indicate that although trained with only one US image data type, our proposed US-Net is capable of restoring images acquired from different body parts and scanning settings with different degradation levels, while exhibiting favorable performance against state-of-the-art image enhancement approaches. Furthermore, utilizing our proposed US-Net as a pre-processing stage for COVID-19 diagnosis results in a gain of 3.6% in diagnostic accuracy. The proposed framework can help improve the accuracy of ultrasound diagnosis.

Computers in biology and medicine

10.1016/j.compbiomed.2022.106385

Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia.

The emergence of COVID-19 as a global pandemic forced researchers worldwide in various disciplines to investigate and propose efficient strategies and/or technologies to prevent COVID-19 from further spreading. One of the main challenges to be overcome is the fast and efficient detection of COVID-19 using deep learning approaches and medical images such as Chest Computed Tomography (CT) and Chest X-ray images. In order to contribute to this challenge, a new dataset was collected in collaboration with "S.E.S Hospital Universitario de Caldas" ( https://hospitaldecaldas.com/ ) from Colombia and organized following the Medical Imaging Data Structure (MIDS) format. The dataset contains 7,307 chest X-ray images divided into 3,077 and 4,230 COVID-19 positive and negative images. Images were subjected to a selection and anonymization process to allow the scientific community to use them freely. Finally, different convolutional neural networks were used to perform technical validation. This dataset contributes to the scientific community by tackling significant limitations regarding data quality and availability for the detection of COVID-19.

Scientific data

10.1038/s41597-022-01576-z 10.1001/jama.2020.1585 10.1109/ACCESS.2020.3028390 10.1109/JAS.2020.1003393 10.1001/archinternmed.2009.427.Radiation 10.1038/s41597-020-00741-6 10.1016/J.MEDIA.2021.102046 10.1016/j.media.2020.101797 10.1016/j.cell.2018.02.010 10.1136/ADC.83.1.82 10.6084/m9.figshare.c.5833484.v1 10.1016/J.MLWA.2021.100138 10.1007/s11263-015-0816-y

Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.

Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations.

SN computer science

10.1007/s42979-022-01464-8 10.1109/TMI.2020.3001810 10.1016/j.asoc.2020.106859 10.1007/s13246-020-00934-8 10.3348/kjr.2020.0536 10.1056/nejmoa2001017 10.3389/frai.2020.00065 10.1016/j.cmpb.2020.105581 10.1109/ICOASE51841.2020.9436542 10.1016/j.compbiomed.2021.104868 10.1016/j.sysarc.2020.101830 10.3390/healthcare8010046 10.1007/s42979-020-00383-w 10.1007/s11042-021-10907-y 10.1038/s41746-020-00372-6 10.3389/fimmu.2020.01441 10.3390/jpm11010028 10.1007/s11042-020-10340-7 10.26355/eurrev_202008_22510 10.1016/j.acra.2020.09.004 10.1186/s12911-020-01316-6 10.1038/s41467-020-18684-2 10.1016/j.ijsu.2010.02.007 10.1016/j.compbiomed.2021.104605 10.1016/j.patrec.2019.11.013 10.1016/j.patcog.2012.10.005 10.1016/j.eswa.2021.115152 10.3390/e23020204 10.1016/j.media.2020.101836 10.1007/s10278-019-00227-x 10.1016/j.compbiomed.2020.104037 10.1002/mp.14676 10.1016/j.cmpbup.2021.100007 10.1109/TIP.2021.3058783 10.1080/01431160600746456 10.1109/ACCESS.2021.3054484 10.1016/j.compbiomed.2021.104319 10.1007/s13246-020-00865-4 10.1016/j.chaos.2020.109944 10.1007/s10489-020-01902-1 10.3233/XST-200720 10.1016/j.ijmedinf.2020.104284 10.31661/jbpe.v0i0.2008-1153 10.1371/journal.pone.0242535 10.1097/RLI.0000000000000748 10.14358/PERS.80.2.000 10.1007/s00330-020-06801-0 10.1148/radiol.2020200230 10.2214/AJR.20.22954 10.1016/j.media.2016.10.004 10.1016/j.chaos.2020.110153 10.1007/s00330-021-07715-1 10.1016/j.compbiomed.2021.104588 10.1038/s41598-021-83237-6 10.1002/mp.14609 10.1007/s00259-020-05075-4 10.1007/s00259-020-04953-1 10.34171/mjiri.34.174 10.1007/s12539-021-00420-z 10.1007/s42399-020-00643-z 10.7759/cureus.10378 10.1097/MD.0000000000023167 10.1148/rg.2020200149 10.1056/nejmp2000929 10.1148/radiol.2462070712 10.1148/radiol.2020200370 10.1016/S2213-2600(20)30076-X 10.1148/radiol.2020202791 10.2214/AJR.20.23513 10.1016/j.ultrasmedbio.2020.07.018 10.1016/j.irbm.2020.07.001 10.1007/s10489-020-01826-w 10.16984/saufenbilder.459659 10.1007/s12539-020-00393-5 10.1109/ACCESS.2020.3016780 10.1007/s42979-021-00605-9 10.1007/s11042-021-10714-5 10.1016/j.compbiomed.2021.104304 10.1371/journal.pone.0235187 10.3390/v12070769 10.1186/s12938-020-00831-x 10.1002/ima.22564 10.1007/s00138-020-01101-5 10.1016/j.cmpb.2020.105532 10.1016/j.imu.2021.100621 10.1038/s41598-021-88807-2 10.1007/s11042-020-09894-3 10.1016/j.bspc.2021.102622 10.1016/j.chemolab.2020.104054 10.1186/s12879-021-05839-9 10.1186/s12938-020-00809-9 10.1016/j.imu.2020.100505 10.1007/s12539-020-00403-6 10.1088/1742-6596/1933/1/012040 10.1007/s00330-021-07957-z 10.1007/s10489-020-01943-6 10.1038/s41598-020-76282-0 10.1038/s41598-020-80261-w 10.32604/cmc.2021.016264 10.1007/s11548-020-02305-w 10.1109/ACCESS.2021.3061058 10.1016/j.bbe.2020.08.008 10.3233/SHTI210223 10.1007/s11548-020-02286-w 10.1049/iet-ipr.2020.1127 10.1007/s11517-020-02299-2 10.1007/s10489-021-02199-4 10.1016/j.compbiomed.2020.103869 10.1016/j.mehy.2020.109761 10.1016/j.compbiomed.2020.103792 10.1016/j.chaos.2020.110495 10.1038/s41598-020-76550-z 10.1016/j.imu.2021.100620 10.7717/peerj-cs.551 10.1109/TII.2021.3057683 10.2196/27468 10.1007/s10140-020-01886-y 10.47611/jsrhs.v9i2.1246 10.1016/j.media.2020.101913 10.1016/j.patrec.2020.09.010 10.1016/j.neucom.2021.03.034 10.1109/TNNLS.2021.3070467 10.1109/EIConCIT50028.2021.9431887 10.1016/j.iot.2021.100377 10.2196/19569 10.1109/ACCESS.2021.3083516 10.9781/ijimai.2020.04.003 10.1007/s10489-020-01867-1 10.29194/njes.23040408 10.7717/PEERJ-CS.345 10.1145/3431804 10.3389/fmed.2021.629134 10.1148/radiol.2020201491 10.32604/cmc.2021.014956 10.1177/2472630320958376 10.1016/j.irbm.2021.01.004 10.1109/ICCC51575.2020.9344870 10.1007/s13246-020-00888-x 10.1007/s00330-020-07225-6 10.1109/TMI.2020.2994908 10.32604/cmc.2021.013228 10.7717/PEERJ-CS.364 10.1016/j.inffus.2020.10.004 10.1016/j.ejrad.2020.109041 10.2196/25535 10.1016/j.bspc.2020.102365 10.3233/XST-200715 10.1109/TNNLS.2021.3054746 10.1016/j.matpr.2021.01.820 10.1038/s41598-020-80936-4 10.1109/TMI.2020.3000314 10.31763/sitech.v1i2.202 10.1016/j.compbiomed.2020.104181 10.1007/s12559-020-09775-9 10.1109/TMI.2020.2996645 10.1038/s41551-021-00704-1 10.1007/s10489-020-02019-1 10.1007/s00146-020-00978-0 10.1136/bmj.m1808 10.1016/j.bspc.2021.102490 10.1016/j.inffus.2020.11.005 10.7717/peerj.10309 10.7150/ijbs.58855 10.1109/ACCESS.2020.3003810 10.1007/s11263-019-01228-7

PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates.

In the current COVID-19 outbreak, efficient testing of COVID-19 individuals has proven vital to limiting and arresting the disease's accelerated spread globally. It has been observed that the severity and mortality ratio of COVID-19 affected patients is at greater risk because of chronic pulmonary diseases. This study looks at radiographic examinations exploiting chest X-ray images (CXI), which have become one of the utmost feasible assessment approaches for pulmonary disorders, including COVID-19. Deep Learning(DL) remains an excellent image classification method and framework; research has been conducted to predict pulmonary diseases with COVID-19 instances by developing DL classifiers with nine class CXI. However, a few claim to have strong prediction results; because of noisy and small data, their recommended DL strategies may suffer from significant deviation and generality failures. Therefore, a unique CNN model(PulDi-COVID) for detecting nine diseases (atelectasis, bacterial-pneumonia, cardiomegaly, covid19, effusion, infiltration, no-finding, pneumothorax, viral-Pneumonia) using CXI has been proposed using the SSE algorithm. Several transfer-learning models: VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, DenseNet169 are trained on CXI of chronic lung diseases and COVID-19 instances. Given that the proposed thirteen SSE ensemble models solved DL's constraints by making predictions with different classifiers rather than a single, we present PulDi-COVID, an ensemble DL model that combines DL with ensemble learning. The PulDi-COVID framework is created by incorporating various snapshots of DL models, which have spearheaded chronic lung diseases with COVID-19 cases identification process with a deep neural network produced CXI by applying a suggested SSE method. That is familiar with the idea of various DL perceptions on different classes. PulDi-COVID findings were compared to thirteen existing studies for nine-class classification using COVID-19. Test results reveal that PulDi-COVID offers impressive outcomes for chronic diseases with COVID-19 identification with a 99.70% accuracy, 98.68% precision, 98.67% recall, 98.67% F1 score, lowest 12 CXIs zero-one loss, 99.24% AUC-ROC score, and lowest 1.33% error rate. Overall test results are superior to the existing Convolutional Neural Network(CNN). To the best of our knowledge, the observed results for nine-class classification are significantly superior to the state-of-the-art approaches employed for COVID-19 detection. Furthermore, the CXI that we used to assess our algorithm is one of the larger datasets for COVID detection with pulmonary diseases. The empirical findings of our suggested approach PulDi-COVID show that it outperforms previously developed methods. The suggested SSE method with PulDi-COVID can effectively fulfill the COVID-19 speedy detection needs with different lung diseases for physicians to minimize patient severity and mortality.

Biomedical signal processing and control

10.1016/j.bspc.2022.104445 10.3389/fmed.2021.588013 10.1109/TII.2021.3057683

Rapid diagnosis of Covid-19 infections by a progressively growing GAN and CNN optimisation.

Covid-19 infections are spreading around the globe since December 2019. Several diagnostic methods were developed based on biological investigations and the success of each method depends on the accuracy of identifying Covid infections. However, access to diagnostic tools can be limited, depending on geographic region and the diagnosis duration plays an important role in treating Covid-19. Since the virus causes pneumonia, its presence can also be detected using medical imaging by Radiologists. Hospitals with X-ray capabilities are widely distributed all over the world, so a method for diagnosing Covid-19 from chest X-rays would present itself. Studies have shown promising results in automatically detecting Covid-19 from medical images using supervised Artificial neural network (ANN) algorithms. The major drawback of supervised learning algorithms is that they require huge amounts of data to train. Also, the radiology equipment is not computationally efficient for deep neural networks. Therefore, we aim to develop a Generative Adversarial Network (GAN) based image augmentation to optimize the performance of custom, light, Convolutional networks used for the classification of Chest X-rays (CXR). A Progressively Growing Generative Adversarial Network (PGGAN) is used to generate synthetic and augmented data to supplement the dataset. We propose two novel CNN architectures to perform the Multi-class classification of Covid-19, healthy and pneumonia affected Chest X-rays. Comparisons have been drawn to the state of the art models and transfer learning methods to evaluate the superiority of the networks. All the models are trained using enhanced and augmented X-ray images and are compared based on classification metrics. The proposed models had extremely high classification metrics with proposed Architectures having test accuracy of 98.78% and 99.2% respectively while having 40% lesser training parameters than their state of the art counterpart. In the present study, a method based on artificial intelligence is proposed, leading to a rapid diagnostic tool for Covid infections based on Generative Adversarial Network (GAN) and Convolutional Neural Networks (CNN). The benefit will be a high accuracy of detection with up to 99% hit rate, a rapid diagnosis, and an accessible Covid identification method by chest X-ray images.

Computer methods and programs in biomedicine

10.1016/j.cmpb.2022.107262

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images.

COVID-19 (SARS-CoV-2), which causes acute respiratory syndrome, is a contagious and deadly disease that has devastating effects on society and human life. COVID-19 can cause serious complications, especially in patients with pre-existing chronic health problems such as diabetes, hypertension, lung cancer, weakened immune systems, and the elderly. The most critical step in the fight against COVID-19 is the rapid diagnosis of infected patients. Computed Tomography (CT), chest X-ray (CXR), and RT-PCR diagnostic kits are frequently used to diagnose the disease. However, due to difficulties such as the inadequacy of RT-PCR test kits and false negative (FN) results in the early stages of the disease, the time-consuming examination of medical images obtained from CT and CXR imaging techniques by specialists/doctors, and the increasing workload on specialists, it is challenging to detect COVID-19. Therefore, researchers have suggested searching for new methods in COVID- 19 detection. In analysis studies with CT and CXR radiography images, it was determined that COVID-19-infected patients experienced abnormalities related to COVID-19. The anomalies observed here are the primary motivation for artificial intelligence researchers to develop COVID-19 detection applications with deep convolutional neural networks. Here, convolutional neural network-based deep learning algorithms from artificial intelligence technologies with high discrimination capabilities can be considered as an alternative approach in the disease detection process. This study proposes a deep convolutional neural network, COVID-DSNet, to diagnose typical pneumonia (bacterial, viral) and COVID-19 diseases from CT, CXR, hybrid CT + CXR images. In the multi-classification study with the CT dataset, 97.60 % accuracy and 97.60 % sensitivity values were obtained from the COVID-DSNet model, and 100 %, 96.30 %, and 96.58 % sensitivity values were obtained in the detection of typical, common pneumonia and COVID-19, respectively. The proposed model is an economical, practical deep learning network that data scientists can benefit from and develop. Although it is not a definitive solution in disease diagnosis, it may help experts as it produces successful results in detecting pneumonia and COVID-19.

Artificial intelligence in medicine

10.1016/j.artmed.2022.102427 10.1016/j.asoc.2020.106859 10.1016/j.virs.2022.09.003 10.1016/j.mtbio.2022.100265 10.1111/1348-0421.12945 10.1002/jmv.27132 10.1038/s41579-021-00573-0 10.1001/jama.2022.14711 10.1038/s41565-022-01177-2 10.1038/s41580-021-00418-x 10.3390/ijms23031716 10.1038/s41580-021-00432-z 10.1016/j.media.2021.102096 10.3390/cancers13020162 10.3389/fimmu.2021.660632 10.1007/s12559-020-09787-5 10.3389/fphar.2021.664349 10.3390/cells10030587 10.1007/s11033-021-06358-1 10.1001/jama.2021.13084 10.1016/j.arbres.2021.06.003 10.1038/s41598-021-96755-0 10.1016/S0140-6736(22)00009-5 10.1016/j.cmpb.2019.105162 10.1148/ryct.2021200564 10.1016/j.ejrad.2020.109147 10.1186/s12890-021-01450-5 10.1007/s12559-020-09779-5 10.1093/bib/bbab412 10.3390/v14020322 10.1007/s00521-020-05410-8 10.1016/j.nupar.2022.01.003 10.1016/j.biochi.2022.01.015 10.1109/ACCESS.2020.3010287 10.1016/j.ijid.2020.10.069 10.3390/biomedicines10020242 10.1016/bs.acr.2020.10.001 10.1084/jem.20202489 10.17305/bjbms.2021.6340 10.1007/s11154-021-09707-4 10.1016/j.jacbts.2021.10.011 10.1001/jamacardio.2020.3557 10.1016/j.chom.2020.05.008 10.1126/science.369.6500.125 10.1093/cid/ciaa644 10.1016/j.asoc.2022.109207 10.1038/s41392-022-00884-5 10.1038/s41579-020-00462-y 10.1136/bmj.n597 10.3390/diagnostics12020467 10.1007/s10311-021-01369-7 10.1056/NEJMc2119236 10.1001/jama.2021.24315 10.1056/NEJMoa2108891 10.1126/science.abn7591 10.1016/j.eclinm.2021.100861 10.1016/j.compbiomed.2021.104742 10.1016/j.bspc.2021.103415 10.3390/diagnostics11111972 10.1007/s00521-020-05636-6 10.1007/s42979-021-00695-5 10.1038/s41598-021-84630-x 10.1038/s41598-021-03889-2 10.3390/s22010372 10.1038/s41598-022-06264-x 10.1016/j.compbiomed.2022.105810 10.1016/j.chaos.2020.110245 10.1016/j.bspc.2022.103977 10.1007/s10489-020-01943-6 10.1007/s00500-021-06579-3 10.1007/s11042-020-10165-4 10.1007/s12559-021-09955-1 10.3390/s22031211 10.1007/s10522-021-09946-7 10.1007/s11042-021-11319-8 10.1038/s41598-020-76550-z 10.48550/arXiv.2003.11597 10.1016/j.patcog.2021.108255 10.1007/s10489-020-01829-7 10.1148/radiol.2020200905 10.1016/j.patrec.2021.08.018 10.1155/2022/6185013 10.1109/CVPR.2016.90 10.1109/CVPR.2017.243 10.1162/neco.1997.9.8.1735 10.3390/s21030832 10.1016/j.chaos.2020.110153 10.1016/j.compbiomed.2021.104319 10.17632/rscbjbr9sj.2 10.1609/aaai.v31i1.11231 10.1109/CVPR.2016.308 10.48550/arXiv.1704.04861 10.1109/CVPR.2018.00907 10.48550/arXiv.1412.6980 10.1016/j.eswa.2020.113909 10.1016/j.compbiomed.2021.105134 10.1016/j.compbiomed.2022.105213 10.1016/j.knosys.2021.106849 10.1038/s41586-022-04569-5 10.1007/s42452-019-1903-4

Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning.

This study presents a novel framework for classifying and visualizing pneumonia induced by COVID-19 from CT images. Although many image classification methods using deep learning have been proposed, in the case of medical image fields, standard classification methods are unable to be used in some cases because the medical images that belong to the same category vary depending on the progression of the symptoms and the size of the inflamed area. In addition, it is essential that the models used be transparent and explainable, allowing health care providers to trust the models and avoid mistakes. In this study, we propose a classification method using contrastive learning and an attention mechanism. Contrastive learning is able to close the distance for images of the same category and generate a better feature space for classification. An attention mechanism is able to emphasize an important area in the image and visualize the location related to classification. Through experiments conducted on two-types of classification using a three-fold cross validation, we confirmed that the classification accuracy was significantly improved; in addition, a detailed visual explanation was achieved comparison with conventional methods.

Scientific reports

10.1038/s41598-022-24936-6 10.1148/radiol.2020200905 10.1016/j.ejrad.2020.109041 10.1109/ACCESS.2020.3005510 10.1016/j.asoc.2020.106885 10.1109/TCBB.2021.3065361 10.1016/j.compbiomed.2020.104037 10.3390/diagnostics11050893 10.1016/j.patcog.2021.107826 10.1016/j.patcog.2021.107848 10.1016/j.media.2021.102105 10.1007/s11263-021-01559-4 10.1148/radiol.2020200905 10.1109/TMI.2020.2995965

A lightweight network for COVID-19 detection in X-ray images.

The Novel Coronavirus 2019 (COVID-19) is a global pandemic which has a devastating impact. Due to its quick transmission, a prominent challenge in confronting this pandemic is the rapid diagnosis. Currently, the commonly-used diagnosis is the specific molecular tests aided with the medical imaging modalities such as chest X-ray (CXR). However, with the large demand, the diagnoses of CXR are time-consuming and laborious. Deep learning is promising for automatically diagnosing COVID-19 to ease the burden on medical systems. At present, the most applied neural networks are large, which hardly satisfy the rapid yet inexpensive requirements of COVID-19 detection. To reduce huge computation and memory demands, in this paper, we focus on implementing lightweight networks for COVID-19 detection in CXR. Concretely, we first augment data based on clinical visual features of CXR from expertise. Then, according to the fact that all the input data are CXR, we design a targeted four-layer network with either 11 × 11 or 3 × 3 kernels to recognize regional features and detail features. A pruning criterion based on the weights importance is also proposed to further prune the network. Experiments on a public COVID-19 dataset validate the effectiveness and efficiency of the proposed method.

Methods (San Diego, Calif.)

10.1016/j.ymeth.2022.11.004

Radiomorphological signs and clinical severity of SARS-CoV-2 lineage B.1.1.7.

We aimed to assess the differences in the severity and chest-CT radiomorphological signs of SARS-CoV-2 B.1.1.7 and non-B.1.1.7 variants. We collected clinical data of consecutive patients with laboratory-confirmed COVID-19 and chest-CT imaging who were admitted to the Emergency Department between September 1- November 13, 2020 (non-B.1.1.7 cases) and March 1-March 18, 2021 (B.1.1.7 cases). We also examined the differences in the severity and radiomorphological features associated with COVID-19 pneumonia. Total pneumonia burden (%), mean attenuation of ground-glass opacities and consolidation were quantified using deep-learning research software. The final population comprised 500 B.1.1.7 and 500 non-B.1.1.7 cases. Patients with B.1.1.7 infection were younger (58.5 ± 15.6 vs 64.8 ± 17.3; Despite B.1.1.7 patients were younger and had fewer comorbidities, they experienced more severe disease than non-B.1.1.7 patients, however, the risk of death was the same between the two groups. Our study provides data on deep-learning based quantitative lung lesion burden and clinical outcomes of patients infected by B.1.1.7 VOC. Our findings might serve as a model for later investigations, as new variants are emerging across the globe.

BJR open

10.1259/bjro.20220016 10.1126/science.abg3055 10.1016/S1473-3099(21)00170-5 10.1136/bmj.n579 10.2807/1560-7917.ES.2021.26.11.2100256 10.1007/s00330-020-06748-2 10.1148/radiol.2020200843 10.2214/AJR.20.22954 10.1148/radiol.2020200370 10.1148/radiol.2020200463 10.1016/S1473-3099(20)30483-7 10.1148/ryct.2020200389 10.1016/j.ejrad.2020.108961 10.1148/radiol.2020200432 10.1136/bmj.m1464 10.1016/S1473-3099(20)30086-4 10.1556/1647.2020.00002 10.1038/s41379-020-0536-x 10.1016/S0140-6736(20)30566-3 10.1038/s41598-020-76550-z 10.1007/s00330-021-07715-1 10.1183/13993003.00775-2020 10.1016/j.compbiomed.2020.103795

3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients.

Chest CT is a useful initial exam in patients with coronavirus disease 2019 (COVID-19) for assessing lung damage. AI-powered predictive models could be useful to better allocate resources in the midst of the pandemic. Our aim was to build a deep-learning (DL) model for COVID-19 outcome prediction inclusive of 3D chest CT images acquired at hospital admission. This retrospective multicentric study included 1051 patients (mean age 69, SD = 15) who presented to the emergency department of three different institutions between 20th March 2020 and 20th January 2021 with COVID-19 confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR). Chest CT at hospital admission were evaluated by a 3D residual neural network algorithm. Training, internal validation, and external validation groups included 608, 153, and 290 patients, respectively. Images, clinical, and laboratory data were fed into different customizations of a dense neural network to choose the best performing architecture for the prediction of mortality, intubation, and intensive care unit (ICU) admission. The AI model tested on CT and clinical features displayed accuracy, sensitivity, specificity, and ROC-AUC, respectively, of 91.7%, 90.5%, 92.4%, and 95% for the prediction of patient's mortality; 91.3%, 91.5%, 89.8%, and 95% for intubation; and 89.6%, 90.2%, 86.5%, and 94% for ICU admission (internal validation) in the testing cohort. The performance was lower in the validation cohort for mortality (71.7%, 55.6%, 74.8%, 72%), intubation (72.6%, 74.7%, 45.7%, 64%), and ICU admission (74.7%, 77%, 46%, 70%) prediction. The addition of the available laboratory data led to an increase in sensitivity for patient's mortality (66%) and specificity for intubation and ICU admission (50%, 52%, respectively), while the other metrics maintained similar performance results. We present a deep-learning model to predict mortality, ICU admittance, and intubation in COVID-19 patients. KEY POINTS: • 3D CT-based deep learning model predicted the internal validation set with high accuracy, sensibility and specificity (> 90%) mortality, ICU admittance, and intubation in COVID-19 patients. • The model slightly increased prediction results when laboratory data were added to the analysis, despite data imbalance. However, the model accuracy dropped when CT images were not considered in the analysis, implying an important role of CT in predicting outcomes.

Journal of digital imaging

10.1007/s10278-022-00734-4

Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning.

The world has been undergoing the most ever unprecedented circumstances caused by the coronavirus pandemic, which is having a devastating global effect in different aspects of life. Since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thereby helping to reduce mortality. While different measures are being used to combat the virus, medical imaging techniques have been examined to support doctors in diagnosing the disease. In this paper, we present a practical solution for the detection of Covid-19 from chest X-ray (CXR) and lung computed tomography (LCT) images, exploiting cutting-edge Machine Learning techniques. As the main classification engine, we make use of EfficientNet and MixNet, two recently developed families of deep neural networks. Furthermore, to make the training more effective and efficient, we apply three transfer learning algorithms. The ultimate aim is to build a reliable expert system to detect Covid-19 from different sources of images, making it be a multi-purpose AI diagnosing system. We validated our proposed approach using four real-world datasets. The first two are CXR datasets consist of 15,000 and 17,905 images, respectively. The other two are LCT datasets with 2,482 and 411,528 images, respectively. The five-fold cross-validation methodology was used to evaluate the approach, where the dataset is split into five parts, and accordingly the evaluation is conducted in five rounds. By each evaluation, four parts are combined to form the training data, and the remaining one is used for testing. We obtained an encouraging prediction performance for all the considered datasets. In all the configurations, the obtained accuracy is always larger than 95.0%. Compared to various existing studies, our approach yields a substantial performance gain. Moreover, such an improvement is statistically significant.

Applied soft computing

10.1016/j.asoc.2022.109851 10.1109/TITS.2021.3053373 10.1016/j.eswa.2021.115519 10.1016/j.eswa.2017.12.020 10.1038/s41591-020-0931-3 10.1007/s11263-015-0816-y 10.1016/j.compag.2018.02.016 10.1186/s40537-016-0043-6 10.1007/11564096_40 10.3390/rs9090907 10.1016/j.compag.2020.105326 10.1101/2020.04.24.20078584 10.1016/j.cell.2020.04.045 10.1016/j.asoc.2020.106691 10.1016/j.tube.2022.102234 10.1016/j.asoc.2021.107323 10.1016/j.compbiomed.2020.103792 10.1101/2020.06.08.20125963 10.1109/CVPR.2016.90 10.1007/s11042-021-10783-6 10.1038/s41598-021-99015-3 10.1007/978-1-4612-4380-9_16 10.1101/2020.06.08.20121541 10.36227/techrxiv.12328061 10.1016/j.compbiomed.2020.103795 10.1148/radiol.2020201491 10.1148/radiol.2020201491 10.1101/2020.08.13.20173997

Mentoring within the medical radiation sciences - Establishing a national program.

The aim of this study was to compare the accuracy and performance of 12 pre-trained deep learning models for classifying covid-19 and normal chest X-ray images from Kaggle. a desktop computer with an Intel CPU i9-10900 2.80GHz and NVIDIA GPU GeForce RTX2070 SUPER, Anaconda3 software with 12 pre-trained models including VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, RestNet50V2, RestNet101V2, RestNet152V2, InceptionRestnetV2, InceptionV3, XceptionV1 and MobileNetV2, covid-19 and normal chest X-ray from Kaggle website. the images were divided into three sets of train, test, and validation sets using a ratio of 70:20:10, respectively. The performance was recorded for each pre-train model with hyperparameters of epoch, batch size, and learning rate as 16, 16 and 0.0001 respectively. The prediction results of each model were recorded and compared. from the results of all 12 pre-trained deep learning model, five models that have highest validation accuracy were DenseNet169, DenseNet201, InceptionV3, DenseNet121 and InceptionRestNetV2, respectively. The top-5 highest accuracy models for classifying the COVID-19 were DenseNet169, DenseNet201, InceptionV3, DenseNet121 and InceptionRestnetV2 with accuracies of 95.4%, 95.07%, 94.73%, 94.51% and 93.61% respectively.

Journal of medical imaging and radiation sciences

10.1016/j.jmir.2022.10.190

Optimal Ensemble learning model for COVID-19 detection using chest X-ray images.

COVID-19 pandemic is the main outbreak in the world, which has shown a bad impact on people's lives in more than 150 countries. The major steps in fighting COVID-19 are identifying the affected patients as early as possible and locating them with special care. Images from radiology and radiography are among the most effective tools for determining a patient's ailment. Recent studies have shown detailed abnormalities of affected patients with COVID-19 in the chest radiograms. The purpose of this work is to present a COVID-19 detection system with three key steps: "(i) preprocessing, (ii) Feature extraction, (iii) Classification." Originally, the input image is given to the preprocessing step as its input, extracting the deep features and texture features from the preprocessed image. Particularly, it extracts the deep features by inceptionv3. Then, the features like proposed Local Vector Patterns (LVP) and Local Binary Pattern (LBP) are extracted from the preprocessed image. Moreover, the extracted features are subjected to the proposed ensemble model based classification phase, including Support Vector Machine (SVM), Convolutional Neural Network (CNN), Optimized Neural Network (NN), and Random Forest (RF). A novel Self Adaptive Kill Herd Optimization (SAKHO) approach is used to properly tune the weight of NN to improve classification accuracy and precision. The performance of the proposed method is then compared to the performance of the conventional approaches using a variety of metrics, including recall, FNR, MCC, FDR, Thread score, FPR, precision, FOR, accuracy, specificity, NPV, FMS, and sensitivity, accordingly.

Biomedical signal processing and control

10.1016/j.bspc.2022.104392 10.1109/TBDATA.2020.3035935 10.1109/ACCESS.2020.3033762 10.3233/HIS-120161 10.1504/IJCSE.2013.053087 10.1007/s40747-020-00216-6 10.1109/ICIP.2014.7025047

Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.

Sensors (Basel, Switzerland)

10.3390/s22228999 10.3389/fmed.2020.00027 10.1097/00004424-196601000-00032 10.1118/1.3013555 10.3390/s22218326 10.1148/83.6.1029 10.1353/pbm.1992.0011 10.1016/j.compmedimag.2007.02.002 10.1002/mp.13764 10.1007/s10489-020-02002-w 10.1164/ajrccm.153.1.8542102 10.12928/telkomnika.v15i4.3163 10.1056/NEJM199105303242205 10.1016/j.eswa.2020.113274 10.1016/j.ijmedinf.2019.06.017 10.1016/j.compbiomed.2019.103345 10.1007/s10278-013-9600-0 10.1080/21681163.2016.1138324 10.22146/ijeis.34713 10.1016/j.jocs.2018.12.003 10.3390/e24070869 10.3390/life12111709 10.3390/s22155880 10.1016/j.conbuildmat.2017.09.110 10.1016/j.compeleceng.2018.07.042 10.1186/s40537-019-0276-2 10.1038/s41598-020-76550-z 10.1016/j.compbiomed.2020.103792 10.1016/j.compbiomed.2020.103795 10.1186/s41256-020-00135-6 10.1101/2020.08.31.20175828 10.1371/journal.pone.0157112 10.1109/ACCESS.2019.2912200 10.1109/MGRS.2018.2853555 10.1109/MCI.2006.329691 10.1016/j.tcs.2005.05.020 10.1016/j.engappai.2014.03.007 10.1016/j.eswa.2006.04.010 10.1016/j.eswa.2015.07.007 10.1016/j.eswa.2014.04.019 10.1371/journal.pone.0140381 10.1016/j.bspc.2019.101678 10.3390/ijerph17124204 10.20944/preprints202003.0300.v1 10.1016/j.chaos.2020.110210 10.1016/j.patrec.2020.09.010 10.1016/j.cor.2021.105359 10.1016/j.ins.2017.12.047 10.1023/B:ANOR.0000039523.95673.33 10.1016/j.imu.2020.100330 10.1155/2021/6621540 10.3390/sym11020157 10.1109/ACCESS.2021.3076756 10.1109/ACCESS.2021.3051723

Dual_Pachi: Attention-based dual path framework with intermediate second order-pooling for Covid-19 detection from chest X-ray images.

Numerous machine learning and image processing algorithms, most recently deep learning, allow the recognition and classification of COVID-19 disease in medical images. However, feature extraction, or the semantic gap between low-level visual information collected by imaging modalities and high-level semantics, is the fundamental shortcoming of these techniques. On the other hand, several techniques focused on the first-order feature extraction of the chest X-Ray thus making the employed models less accurate and robust. This study presents Dual_Pachi: Attention Based Dual Path Framework with Intermediate Second Order-Pooling for more accurate and robust Chest X-ray feature extraction for Covid-19 detection. Dual_Pachi consists of 4 main building Blocks; Block one converts the received chest X-Ray image to CIE LAB coordinates (L & AB channels which are separated at the first three layers of a modified Inception V3 Architecture.). Block two further exploit the global features extracted from block one via a global second-order pooling while block three focuses on the low-level visual information and the high-level semantics of Chest X-ray image features using a multi-head self-attention and an MLP Layer without sacrificing performance. Finally, the fourth block is the classification block where classification is done using fully connected layers and SoftMax activation. Dual_Pachi is designed and trained in an end-to-end manner. According to the results, Dual_Pachi outperforms traditional deep learning models and other state-of-the-art approaches described in the literature with an accuracy of 0.96656 (Data_A) and 0.97867 (Data_B) for the Dual_Pachi approach and an accuracy of 0.95987 (Data_A) and 0.968 (Data_B) for the Dual_Pachi without attention block model. A Grad-CAM-based visualization is also built to highlight where the applied attention mechanism is concentrated.

Computers in biology and medicine

10.1016/j.compbiomed.2022.106324 10.1142/S0218339020500096 10.7150/ijbs.45053 10.1016/j.jare.2020.03.005 10.1016/j.cpcardiol.2020.100618 10.1016/j.diii.2020.03.014 10.1016/S1473-3099(20)30190-0 10.1002/jmv.25721 10.1002/jmv.25786 10.1148/radiol.2020200642 10.1016/j.jcct.2011.07.001 10.1109/prai53619.2021.9551094 10.1007/s10278-017-9983-4 10.3390/s20113243 10.1016/j.compbiomed.2020.103795 10.1007/s10278-019-00227-x 10.1007/s13246-020-00888-x 10.1093/cid/ciaa1383 10.1016/j.compbiomed.2020.103869 10.1016/j.bspc.2021.102696 10.1016/j.eswa.2021.114576 10.3390/diagnostics12051152 10.1016/j.sciaf.2022.e01151 10.1016/j.patcog.2020.107613 10.1016/j.compbiomed.2020.103792 10.1016/j.pdpdt.2021.102473 10.1038/s41598-020-76550-z 10.1016/j.patrec.2020.09.010 10.1007/s00330-021-07715-1 10.1007/s10044-021-00984-y 10.1016/j.media.2020.101794 10.1007/s10489-020-01902-1 10.1007/s13246-020-00865-4 10.1109/SSCI47803.2020.9308571 10.1016/j.chaos.2020.109944 10.33889/IJMEMS.2020.5.4.052 10.1109/ICCC51575.2020.9344870 10.1007/s42600-021-00151-6 10.1016/j.chaos.2020.110495 10.1016/j.compbiomed.2020.104181 10.1007/s10916-021-01745-4 10.1016/j.compbiomed.2021.104816 10.1016/j.knosys.2022.108207 10.3233/idt-210002 10.1007/s12065-021-00679-7 10.1016/j.eswa.2020.114054 10.1007/s00354-021-00152-0 10.34133/2019/9237136 10.1109/ACCESS.2020.3010287 10.3390/covid1010034 10.1016/j.cmpb.2020.105581 10.1109/JBHI.2021.3058293 10.1016/j.asoc.2022.108867 10.1109/JBHI.2021.3074893 10.3390/s22031211 10.3390/ijerph182111086 10.1007/s00521-019-04332-4

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images.

According to research, classifiers and detectors are less accurate when images are blurry, have low contrast, or have other flaws which raise questions about the machine learning model's ability to recognize items effectively. The chest X-ray image has proven to be the preferred image modality for medical imaging as it contains more information about a patient. Its interpretation is quite difficult, nevertheless. The goal of this research is to construct a reliable deep-learning model capable of producing high classification accuracy on chest x-ray images for lung diseases. To enable a thorough study of the chest X-ray image, the suggested framework first derived richer features using an ensemble technique, then a global second-order pooling is applied to further derive higher global features of the images. Furthermore, the images are then separated into patches and position embedding before analyzing the patches individually via a vision transformer approach. The proposed model yielded 96.01% sensitivity, 96.20% precision, and 98.00% accuracy for the COVID-19 Radiography Dataset while achieving 97.84% accuracy, 96.76% sensitivity and 96.80% precision, for the Covid-ChestX-ray-15k dataset. The experimental findings reveal that the presented models outperform traditional deep learning models and other state-of-the-art approaches provided in the literature.

Bioengineering (Basel, Switzerland)

10.3390/bioengineering9110709 10.3390/bioengineering9070305 10.1007/s42399-020-00527-2 10.1007/s00415-020-10067-3 10.1007/s42399-020-00383-0 10.1155/2022/9210947 10.1109/ACCESS.2022.3194152 10.1155/2022/5641727 10.1016/j.jare.2022.08.021 10.1186/s12890-020-01286-5 10.1007/s11042-019-08394-3 10.3390/bioengineering9040172 10.1155/2022/5718501 10.1109/ACCESS.2019.2928020 10.3390/bios12060427 10.1155/2022/3599246 10.2174/1871527319666201110124954 10.3390/app10217410 10.2174/1389450121666201027125828 10.1145/3465055 10.32604/cmc.2021.014134 10.3390/diagnostics10090649 10.1155/2019/4180949 10.1007/s10916-021-01745-4 10.1016/j.compeleceng.2019.08.004 10.1016/j.cmpb.2019.06.023 10.3390/app10020559 10.1016/j.measurement.2020.108046 10.1016/j.bspc.2020.102365 10.1016/j.compbiomed.2020.103792 10.1016/j.chaos.2021.110713 10.1016/j.compbiomed.2021.104375 10.1016/j.chaos.2021.110749 10.1016/j.patcog.2021.108255 10.1007/s10044-021-00984-y 10.1038/s41598-020-76550-z 10.1007/s11063-022-10834-5 10.1109/ACCESS.2020.3010287 10.3390/covid1010034 10.1155/2022/9475162 10.3389/fnins.2021.754058 10.1109/ACCESS.2022.3212120 10.1155/2022/3408501 10.3390/app12031344 10.31083/j.jin2101020 10.1016/b978-0-323-99031-8.00012-0 10.3390/diagnostics12112815 10.1016/j.cmpb.2020.105581 10.1109/JBHI.2021.3058293 10.1016/j.asoc.2022.108867 10.1109/JBHI.2021.3074893 10.3390/s22031211 10.18280/ts.380337 10.18201/ijisae.2020466310 10.3233/XST-211005 10.1016/j.cell.2018.02.010 10.1007/s12559-020-09787-5 10.1016/j.chaos.2020.109944

Portable, Automated and Deep-Learning-Enabled Microscopy for Smartphone-Tethered Optical Platform Towards Remote Homecare Diagnostics: A Review.

Globally new pandemic diseases induce urgent demands for portable diagnostic systems to prevent and control infectious diseases. Smartphone-based portable diagnostic devices are significantly efficient tools to user-friendly connect personalized health conditions and collect valuable optical information for rapid diagnosis and biomedical research through at-home screening. Deep learning algorithms for portable microscopes also help to enhance diagnostic accuracy by reducing the imaging resolution gap between benchtop and portable microscopes. This review highlighted recent progress and continued efforts in a smartphone-tethered optical platform through portable, automated, and deep-learning-enabled microscopy for personalized diagnostics and remote monitoring. In detail, the optical platforms through smartphone-based microscopes and lens-free holographic microscopy are introduced, and deep learning-based portable microscopic imaging is explained to improve the image resolution and accuracy of diagnostics. The challenges and prospects of portable optical systems with microfluidic channels and a compact microscope to screen COVID-19 in the current pandemic are also discussed. It has been believed that this review offers a novel guide for rapid diagnosis, biomedical imaging, and digital healthcare with low cost and portability.

Small methods

10.1002/smtd.202200979

A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques.

COVID-19 pandemic created a turmoil across nations due to Severe Acute Respiratory Syndrome Corona virus-1(SARS - Co-V-2). The severity of COVID-19 symptoms is starting from cold, breathing problems, issues in respiratory system which may also lead to life threatening situations. This disease is widely contaminating and transmitted from man-to-man. The contamination is spreading when the human organs like eyes, nose, and mouth get in contact with contaminated fluids. This virus can be screened through performing a nasopharyngeal swab test which is time consuming. So the physicians are preferring the fast detection methods like chest radiography images and CT scans. At times some confusion in finding out the accurate disorder from chest radiography images can happen. To overcome this issue this study reviews several deep learning and machine learning procedures to be implemented in X-ray images of chest. This also helps the professionals to find out the other types of malfunctions happening in the chest other than COVID-19 also. This review can act as a guidance to the doctors and radiologists in identifying the COVID-19 and other types of viruses causing illness in the human anatomy and can provide aid soon.

Network (Bristol, England)

10.1080/0954898X.2022.2147231

Deep progressive learning achieves whole-body low-dose

To validate a total-body PET-guided deep progressive learning reconstruction method (DPR) for low-dose List-mode data from the retrospective study (n = 26) were rebinned into short-duration scans and reconstructed with DPR. The standard uptake value (SUV) and tumor-to-liver ratio (TLR) in lesions and coefficient of variation (COV) in the liver in the DPR images were compared to the reference (OSEM images with full-duration data). In the prospective study, another 41 patients were injected with 1/3 of the activity based on the retrospective results. The DPR images (DPR_1/3(p)) were generated and compared with the reference (OSEM images with extended acquisition time). The SUV and COV were evaluated in three selected organs: liver, blood pool and muscle. Quantitative analyses were performed with lesion SUV and TLR, furthermore on small lesions (≤ 10 mm in diameter). Additionally, a 5-point Likert scale visual analysis was performed on the following perspectives: contrast, noise and diagnostic confidence. In the retrospective study, the DPR with one-third duration can maintain the image quality as the reference. In the prospective study, good agreement among the SUVs was observed in all selected organs. The quantitative results showed that there was no significant difference in COV between the DPR_1/3(p) group and the reference, while the visual analysis showed no significant differences in image contrast, noise and diagnostic confidence. The lesion SUVs and TLRs in the DPR_1/3(p) group were significantly enhanced compared with the reference, even for small lesions. The proposed DPR method can reduce the administered activity of

EJNMMI physics

10.1186/s40658-022-00508-5 10.1007/s00259-014-2961-x 10.1016/S0377-1237(09)80099-3 10.2967/jnumed.107.047787 10.1007/s12350-016-0522-3 10.1016/j.nuclcard.2007.04.006 10.1136/jnnp.2003.028175 10.2967/jnumed.117.200790 10.1097/RLU.0000000000003075 10.1007/s00259-020-05167-1 10.1186/s13550-020-00695-1 10.1007/s00259-021-05197-3.10.1007/s00259-021-05197-3 10.1007/s00259-021-05478-x 10.1109/TRPMS.2020.3014786 10.1088/1361-6560/abfb17 10.1007/s00247-006-0191-5 10.1007/s00247-009-1404-5 10.1259/bjr/01948454 10.1007/s00259-020-05091-4 10.1007/s00259-021-05304-4 10.1007/s00259-021-05462-5 10.1007/s00259-021-05537-3 10.2967/jnumed.121.262038 10.1007/s00259-021-05592-w 10.1259/bjr.20201356 10.1186/s13550-019-0536-3 10.1007/s00259-017-3893-z 10.1186/s13550-019-0565-y

COVID-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization.

The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these images is a difficult process, so computerized techniques are widely used. Deep convolutional neural networks (DCNNs) are a type of machine learning that is frequently used in computer vision applications, particularly in medical imaging, to detect and classify infected regions. These techniques can assist medical personnel in the detection of patients with COVID-19. In this article, a Bayesian optimized DCNN and explainable AI-based framework is proposed for the classification of COVID-19 from the chest X-ray images. The proposed method starts with a multi-filter contrast enhancement technique that increases the visibility of the infected part. Two pre-trained deep models, namely, EfficientNet-B0 and MobileNet-V2, are fine-tuned according to the target classes and then trained by employing Bayesian optimization (BO). Through BO, hyperparameters have been selected instead of static initialization. Features are extracted from the trained model and fused using a slicing-based serial fusion approach. The fused features are classified using machine learning classifiers for the final classification. Moreover, visualization is performed using a Grad-CAM that highlights the infected part in the image. Three publically available COVID-19 datasets are used for the experimental process to obtain improved accuracies of 98.8, 97.9, and 99.4%, respectively.

Frontiers in public health

10.3389/fpubh.2022.1046296 10.3390/s21020455 10.32604/cmc.2022.020140 10.1111/exsy.12776 10.1016/j.compbiomed.2022.105233 10.1155/2022/7672196 10.3389/fcomp.2020.00005 10.1016/j.bbi.2020.04.081 10.1148/radiol.2020200230 10.7717/peerj-cs.655 10.1109/TMI.2020.3040950 10.1109/TMI.2020.2993291 10.1109/TMI.2016.2528162 10.1155/2022/7377502 10.1016/j.media.2017.07.005 10.1038/nature14539 10.1148/radiol.2017162326 10.1007/s11263-015-0816-y 10.48550/arXiv.1602.07360 10.3389/fpubh.2022.948205 10.1155/2021/2560388 10.1016/j.compbiomed.2022.105213 10.3389/fmed.2020.00427 10.1371/journal.pone.0242535 10.1007/s13755-020-00119-3 10.3390/info11090419 10.1155/2020/8828855 10.1155/2022/4254631 10.1007/s00530-021-00826-1 10.1007/s42600-020-00120-5 10.1155/2022/1307944 10.3390/jpm12020309 10.3390/s21217286 10.1016/j.ecoinf.2020.101182 10.1007/s12530-020-09345-2 10.1016/j.chaos.2020.110511 10.1007/s00521-022-07052-4 10.1016/j.asoc.2020.106580 10.1016/j.compbiomed.2022.105244

Efficient-ECGNet framework for COVID-19 classification and correlation prediction with the cardio disease through electrocardiogram medical imaging.

In the last 2 years, we have witnessed multiple waves of coronavirus that affected millions of people around the globe. The proper cure for COVID-19 has not been diagnosed as vaccinated people also got infected with this disease. Precise and timely detection of COVID-19 can save human lives and protect them from complicated treatment procedures. Researchers have employed several medical imaging modalities like CT-Scan and X-ray for COVID-19 detection, however, little concentration is invested in the ECG imaging analysis. ECGs are quickly available image modality in comparison to CT-Scan and X-ray, therefore, we use them for diagnosing COVID-19. Efficient and effective detection of COVID-19 from the ECG signal is a complex and time-taking task, as researchers usually convert them into numeric values before applying any method which ultimately increases the computational burden. In this work, we tried to overcome these challenges by directly employing the ECG images in a deep-learning (DL)-based approach. More specifically, we introduce an Efficient-ECGNet method that presents an improved version of the EfficientNetV2-B4 model with additional dense layers and is capable of accurately classifying the ECG images into healthy, COVID-19, myocardial infarction (MI), abnormal heartbeats (AHB), and patients with Previous History of Myocardial Infarction (PMI) classes. Moreover, we introduce a module to measure the similarity of COVID-19-affected ECG images with the rest of the diseases. To the best of our knowledge, this is the first effort to approximate the correlation of COVID-19 patients with those having any previous or current history of cardio or respiratory disease. Further, we generate the heatmaps to demonstrate the accurate key-points computation ability of our method. We have performed extensive experimentation on a publicly available dataset to show the robustness of the proposed approach and confirmed that the Efficient-ECGNet framework is reliable to classify the ECG-based COVID-19 samples.

Frontiers in medicine

10.3389/fmed.2022.1005920 10.1016/j.ejim.2020.06.015 10.1007/s15010-020-01401-y 10.1016/j.jinf.2020.02.016 10.1007/s13755-021-00169-1 10.7717/peerj-cs.386 10.1016/j.compeleceng.2020.106960 10.1109/ICAI52203.2021.9445258 10.1016/j.asoc.2020.106691 10.1016/j.asoc.2021.107323 10.1016/j.asoc.2021.107160 10.1155/2021/9619079 10.1155/2022/1575303 10.1016/j.compbiomed.2021.104575 10.1016/j.chaos.2020.110190 10.1016/j.bspc.2021.102588 10.1016/j.irbm.2021.01.004 10.1002/jemt.23578 10.1016/j.imu.2020.100360 10.1007/s10489-020-01826-w 10.1007/s00521-021-05910-1 10.1007/s10489-020-01902-1 10.1007/s10489-020-01943-6 10.1007/s11356-020-10133-3 10.21203/rs.3.rs-646890/v1 10.1007/s00521-020-05410-8 10.1186/s12911-021-01521-x 10.1109/JIOT.2021.3051080 10.1016/j.dib.2021.106762 10.1109/CVPR.2015.7298594 10.1016/j.inpa.2020.04.004 10.1109/CVPR.2016.90 10.1109/CVPR.2017.243 10.1109/CVPR.2018.00474 10.1109/CVPR.2018.00745 10.1109/LSP.2016.2573042 10.1109/TPAMI.2005.165 10.1049/cit2.12101 10.1007/s13369-021-06182-6 10.1007/s42979-020-0114-9 10.1109/ICCASIT50869.2020.9368658 10.1145/3341095

COVID-19 Data Analytics Using Extended Convolutional Technique.

The healthcare system, lifestyle, industrial growth, economy, and livelihood of human beings worldwide were affected due to the triggered global pandemic by the COVID-19 virus that originated and was first reported in Wuhan city, Republic Country of China. COVID cases are difficult to predict and detect in their early stages, and their spread and mortality are uncontrollable. The reverse transcription polymerase chain reaction (RT-PCR) is still the first and foremost diagnostical methodology accepted worldwide; hence, it creates a scope of new diagnostic tools and techniques of detection approach which can produce effective and faster results compared with its predecessor. Innovational through current studies that complement the existence of the novel coronavirus (COVID-19) to findings in the thorax (chest) X-ray imaging, the projected research's method makes use of present deep learning (DL) models with the integration of various frameworks such as GoogleNet, U-Net, and ResNet50 to novel method those X-ray images and categorize patients as the corona positive (COVID + ve) or the corona negative (COVID -ve). The anticipated technique entails the pretreatment phase through dissection of the lung, getting rid of the environment which does now no longer provide applicable facts and can provide influenced consequences; then after this, the preliminary degree comes up with the category version educated below the switch mastering system; and in conclusion, consequences are evaluated and interpreted through warmth maps visualization. The proposed research method completed a detection accuracy of COVID-19 at around 99%.

Interdisciplinary perspectives on infectious diseases

10.1155/2022/4578838 10.1016/j.mlwa.2021.100138 10.1155/2021/6621607 10.1371/journal.pone.0262052 10.1148/radiol.2020200642 10.23750/abm.v91i1.9397 10.1109/access.2020.2997311 10.1056/NEJMoa2002032 10.1155/2022/4838009 10.1001/jama.2020.3786 10.21227/4kcm-m312 10.3390/ijerph18063056 10.1007/s13755-021-00152-w 10.1007/s13755-021-00158-4 10.3390/healthcare9050522

Calibrated bagging deep learning for image semantic segmentation: A case study on COVID-19 chest X-ray image.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff for facilitating a diagnosis of COVID-19 in a more efficient and comprehensive manner. As a breakthrough of artificial intelligence (AI), deep learning has been applied to perform COVID-19 infection region segmentation and disease classification by analyzing CXR and CT data. However, prediction uncertainty of deep learning models for these tasks, which is very important to safety-critical applications like medical image processing, has not been comprehensively investigated. In this work, we propose a novel ensemble deep learning model through integrating bagging deep learning and model calibration to not only enhance segmentation performance, but also reduce prediction uncertainty. The proposed method has been validated on a large dataset that is associated with CXR image segmentation. Experimental results demonstrate that the proposed method can improve the segmentation performance, as well as decrease prediction uncertainty.

PloS one

10.1371/journal.pone.0276250 10.1002/ima.22469 10.1145/3411760 10.33889/IJMEMS.2020.5.4.052 10.1007/s13246-020-00865-4 10.1007/s10044-021-00984-y 10.3390/ijerph18063056 10.1097/RTI.0000000000000532 10.3390/s21217116 10.1016/j.compbiomed.2021.104984 10.1016/j.bspc.2021.103182 10.1175/1520-0450(1967)006<0748:VOPPAB>2.0.CO;2 10.1177/0962280213497434 10.1136/amiajnl-2011-000291 10.1007/BF00058655 10.1186/s12880-020-00529-5 10.1148/ryct.2020200082 10.1038/nature21056

CNN Features and Optimized Generative Adversarial Network for COVID-19 Detection from Chest X-Ray Images.

Coronavirus is a RNA type virus, which makes various respiratory infections in both human as well as animals. In addition, it could cause pneumonia in humans. The Coronavirus affected patients has been increasing day to day, due to the wide spread of diseases. As the count of corona affected patients increases, most of the regions are facing the issue of test kit shortage. In order to resolve this issue, the deep learning approach provides a better solution for automatically detecting the COVID-19 disease. In this research, an optimized deep learning approach, named Henry gas water wave optimization-based deep generative adversarial network (HGWWO-Deep GAN) is developed. Here, the HGWWO algorithm is designed by the hybridization of Henry gas solubility optimization (HGSO) and water wave optimization (WWO) algorithm. The pre-processing method is carried out using region of interest (RoI) and median filtering in order to remove the noise from the images. Lung lobe segmentation is carried out using U-net architecture and lung region extraction is done using convolutional neural network (CNN) features. Moreover, the COVID-19 detection is done using Deep GAN trained by the HGWWO algorithm. The experimental result demonstrates that the developed model attained the optimal performance based on the testing accuracy of 0.9169, sensitivity of 0.9328, and specificity of 0.9032.

Critical reviews in biomedical engineering

10.1615/CritRevBiomedEng.2022042286

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models.

Deep learning techniques, in particular generative models, have taken on great importance in medical image analysis. This paper surveys fundamental deep learning concepts related to medical image generation. It provides concise overviews of studies which use some of the latest state-of-the-art models from last years applied to medical images of different injured body areas or organs that have a disease associated with (e.g., brain tumor and COVID-19 lungs pneumonia). The motivation for this study is to offer a comprehensive overview of artificial neural networks (NNs) and deep generative models in medical imaging, so more groups and authors that are not familiar with deep learning take into consideration its use in medicine works. We review the use of generative models, such as generative adversarial networks and variational autoencoders, as techniques to achieve semantic segmentation, data augmentation, and better classification algorithms, among other purposes. In addition, a collection of widely used public medical datasets containing magnetic resonance (MR) images, computed tomography (CT) scans, and common pictures is presented. Finally, we feature a summary of the current state of generative models in medical image including key features, current challenges, and future research paths.

Neural computing & applications

10.1007/s00521-022-07953-4 10.1016/j.artmed.2021.102164 10.1016/j.artmed.2021.102165 10.1145/3464423 10.1145/3465398 10.1007/s00521-022-07099-3 10.1007/s00521-022-06960-9 10.1016/j.artmed.2020.101938 10.1016/j.neunet.2014.09.003 10.1016/j.compmedimag.2019.04.005 10.1245/ASO.2004.04.018 10.3109/02841851.2010.498444 10.7314/APJCP.2012.13.3.927 10.1136/bjo.80.11.940 10.1136/bjo.83.8.902 10.1016/j.compbiomed.2005.01.006 10.1109/10.959322 10.1016/j.compbiomed.2021.104319 10.1038/nature14539 10.1016/0730-725X(93)90417-C 10.1093/clinchem/48.10.1828 10.1245/ASO.2004.03.007 10.1179/016164104773026534 10.1145/3065386 10.1007/s00521-022-06953-8 10.1007/BF00344251 10.1109/42.476112 10.1016/j.media.2017.07.005 10.1007/978-3-319-46448-0_2 10.1016/j.ejca.2019.04.001 10.1371/journal.pmed.1002730 10.1016/j.cmpb.2020.105532 10.1016/j.media.2018.10.006 10.1016/j.media.2019.01.012 10.1016/j.media.2019.101557 10.1016/j.bspc.2021.102901 10.1007/s10278-019-00227-x 10.1016/j.media.2020.101884 10.1016/j.neucom.2019.02.003 10.1109/TPAMI.2021.3059968 10.1007/s10462-020-09854-1 10.1016/j.compeleceng.2021.107036 10.3390/diagnostics11020169 10.1016/j.compbiomed.2021.104699 10.3389/fgene.2021.639930 10.1038/s41592-020-01008-z 10.1155/2021/6625688 10.1109/TMI.2020.2995508 10.1016/j.neunet.2021.03.006 10.1109/JBHI.2020.2986926 10.1109/ACCESS.2019.2899108 10.1016/j.bspc.2019.101678 10.1016/j.bspc.2019.101641 10.1002/mp.13927 10.1002/mp.14006 10.1016/j.remnie.2016.07.002 10.1007/s00259-020-04816-9 10.1186/s13195-021-00797-5 10.1016/j.media.2020.101716 10.1016/j.cmpb.2020.105568 10.1007/s12539-020-00403-6 10.2174/1573405616666200604163954 10.1002/mp.15044 10.1016/j.cmpb.2021.106018 10.1561/2200000056 10.1007/s11548-018-1898-0 10.1016/j.ejmp.2021.02.013 10.1016/j.artmed.2020.102006 10.1007/s10278-020-00413-2 10.1016/j.media.2020.101952 10.1212/WNL.0b013e3181cb3e25 10.1007/s10916-019-1475-2 10.1016/j.compbiomed.2020.103764 10.1371/journal.pone.0140381 10.1016/j.compbiomed.2019.103345 10.1109/TMI.2014.2377694 10.1109/TMI.2018.2867350 10.1016/j.compbiomed.2020.103774 10.1016/j.acra.2011.09.014 10.12913/22998624/137964 10.1016/j.cell.2018.02.010 10.1016/j.cmpb.2020.105581 10.1016/j.media.2020.101794 10.1118/1.3528204 10.1038/s41597-021-00815-z 10.3758/BRM.42.1.351 10.1007/s13246-020-00865-4 10.1016/j.artmed.2020.101880 10.1016/j.media.2021.102327 10.1109/TMI.2022.3147426 10.1016/j.media.2022.102479 10.1109/ACCESS.2022.3172975 10.1016/j.dib.2022.108258

EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images.

The COVID-19 pandemic has had a significant impact on many lives and the economies of many countries since late December 2019. Early detection with high accuracy is essential to help break the chain of transmission. Several radiological methodologies, such as CT scan and chest X-ray, have been employed in diagnosing and monitoring COVID-19 disease. Still, these methodologies are time-consuming and require trial and error. Machine learning techniques are currently being applied by several studies to deal with COVID-19. This study exploits the latent embeddings of variational autoencoders combined with ensemble techniques to propose three effective EVAE-Net models to detect COVID-19 disease. Two encoders are trained on chest X-ray images to generate two feature maps. The feature maps are concatenated and passed to either a combined or individual reparameterization phase to generate latent embeddings by sampling from a distribution. The latent embeddings are concatenated and passed to a classification head for classification. The COVID-19 Radiography Dataset from Kaggle is the source of chest X-ray images. The performances of the three models are evaluated. The proposed model shows satisfactory performance, with the best model achieving 99.19% and 98.66% accuracy on four classes and three classes, respectively.

Diagnostics (Basel, Switzerland)

10.3390/diagnostics12112569 10.1148/radiol.2020200432 10.1109/ACCESS.2020.3033762 10.1128/JCM.01438-20 10.1016/j.knosys.2020.106647 10.3238/arztebl.2014.0181 10.1007/s11548-019-01917-1 10.1016/j.jemermed.2020.04.004 10.1148/radiol.2020201160 10.14245/ns.1938396.198 10.1038/s41746-020-0273-z 10.1109/TMI.2016.2535865 10.1109/ISBI.2018.8363572 10.1016/j.measurement.2019.05.076 10.1016/j.bspc.2022.103848 10.1016/j.bspc.2022.103595 10.1016/j.neucom.2022.01.055 10.1016/j.jksuci.2020.12.010 10.1038/323533a0 10.1561/2200000056 10.21437/Interspeech.2016-1183 10.1016/S0140-6736(20)30304-4 10.1109/TCYB.2020.2990162 10.1093/jtm/taaa080 10.3390/healthcare8010046 10.1038/s41467-020-17280-8 10.1016/j.compbiomed.2020.103869 10.3390/s21175813 10.1148/radiol.2020200905 10.3390/app12126269 10.1016/j.compbiomed.2020.103792 10.1109/CVPR.2017.690 10.1016/j.cmpb.2020.105581 10.1007/s12530-021-09385-2 10.1109/TNNLS.2021.3070467 10.3390/sym14071398 10.1007/s11517-020-02299-2 10.1007/s12539-020-00403-6 10.3390/healthcare10071313 10.1016/j.compbiomed.2022.105233 10.1007/s13246-020-00865-4 10.1109/CVPR.2017.243 10.1007/s13755-021-00140-0 10.1038/s41598-020-76550-z 10.1109/TCBB.2021.3065361 10.21037/atm.2020.03.132 10.1007/s10489-020-01900-3 10.1016/j.asoc.2020.106912 10.1109/IJCNN.2015.7280578 10.1109/ICCCI50826.2021.9402545 10.1155/2021/5527923 10.1007/978-3-030-74575-2_14 10.1111/j.1365-2818.2010.03415.x 10.1109/ICACCI.2014.6968381 10.32604/cmc.2022.020698 10.1109/TBDATA.2017.2717439 10.14569/IJACSA.2021.0120717 10.1155/2018/3078374 10.3390/jimaging7050083 10.1148/ryai.2021200218 10.1111/srt.13145 10.1007/s10489-020-01813-1 10.1016/j.micpro.2020.103280 10.1016/j.neucom.2015.08.104 10.1016/j.cmpb.2022.106883 10.1016/j.bbe.2021.09.004 10.1016/j.compbiomed.2021.105134 10.1155/2022/5329014 10.32604/cmc.2021.018449 10.1007/s10489-020-02002-w 10.1145/3451357 10.1016/j.patrec.2021.08.018 10.1038/s41598-022-05532-0 10.1016/j.bspc.2021.103326 10.1016/j.compmedimag.2021.102008 10.1016/j.bspc.2022.103677 10.1016/j.compbiomed.2022.105340 10.1016/j.jksuci.2021.07.005 10.1016/j.jksuci.2022.04.006 10.1016/j.compbiomed.2021.104319 10.1109/ACCESS.2020.3010287 10.1016/j.compbiomed.2021.104834 10.1016/j.compbiomed.2022.105244 10.1016/j.bspc.2022.103860

Generative adversarial network based data augmentation for CNN based detection of Covid-19.

Covid-19 has been a global concern since 2019, crippling the world economy and health. Biological diagnostic tools have since been developed to identify the virus from bodily fluids and since the virus causes pneumonia, which results in lung inflammation, the presence of the virus can also be detected using medical imaging by expert radiologists. The success of each diagnostic method is measured by the hit rate for identifying Covid infections. However, the access for people to each diagnosis tool can be limited, depending on the geographic region and, since Covid treatment denotes a race against time, the diagnosis duration plays an important role. Hospitals with X-ray opportunities are widely distributed all over the world, so a method investigating lung X-ray images for possible Covid-19 infections would offer itself. Promising results have been achieved in the literature in automatically detecting the virus using medical images like CT scans and X-rays using supervised artificial neural network algorithms. One of the major drawbacks of supervised learning models is that they require enormous amounts of data to train, and generalize on new data. In this study, we develop a Swish activated, Instance and Batch normalized Residual U-Net GAN with dense blocks and skip connections to create synthetic and augmented data for training. The proposed GAN architecture, due to the presence of instance normalization and swish activation, can deal with the randomness of luminosity, that arises due to different sources of X-ray images better than the classical architecture and generate realistic-looking synthetic data. Also, the radiology equipment is not generally computationally efficient. They cannot efficiently run state-of-the-art deep neural networks such as DenseNet and ResNet effectively. Hence, we propose a novel CNN architecture that is 40% lighter and more accurate than state-of-the-art CNN networks. Multi-class classification of the three classes of chest X-rays (CXR), ie Covid-19, healthy and Pneumonia, is performed using the proposed model which had an extremely high test accuracy of 99.2% which has not been achieved in any previous studies in the literature. Based on the mentioned criteria for developing Corona infection diagnosis, in the present study, an Artificial Intelligence based method is proposed, resulting in a rapid diagnostic tool for Covid infections based on generative adversarial and convolutional neural networks. The benefit will be a high accuracy of lung infection identification with 99% accuracy. This could lead to a support tool that helps in rapid diagnosis, and an accessible Covid identification method using CXR images.

Scientific reports

10.1038/s41598-022-23692-x 10.1016/S1473-3099(20)30120-1 10.1148/radiol.2020201160 10.1038/s42003-020-01535-7 10.1155/2021/3366057 10.1515/cdbme-2020-3051 10.1016/j.cmpb.2021.106279 10.1016/j.medengphy.2016.10.010 10.1016/j.cma.2020.112989 10.1016/j.mechrescom.2021.103817 10.1016/j.euromechsol.2006.12.002 10.1016/j.mechmat.2005.06.001 10.1038/s41598-021-93543-8 10.1016/j.imu.2020.100412 10.1016/j.imu.2020.100505 10.1016/j.ijmedinf.2020.104284 10.1148/radiol.2017162326 10.1109/ACCESS.2020.2994762 10.1007/s00521-022-06918-x 10.1016/j.compbiomed.2020.103792 10.1007/s10044-020-00950-0 10.3390/diagnostics12020267 10.1109/TMI.2020.2995518 10.3390/app112210528 10.1007/s00500-019-04602-2 10.1016/j.imu.2021.100779 10.1038/s41598-021-87994-2 10.1109/TMI.2013.2290491 10.1109/TMI.2013.2284099 10.1186/s40537-021-00444-8 10.1007/s13246-020-00865-4 10.1371/journal.pone.0262052

CXR-Net: A Multitask Deep Learning Network for Explainable and Accurate Diagnosis of COVID-19 Pneumonia From Chest X-Ray Images.

Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first-line imaging technique for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Currently, many deep learning (DL) models have been proposed to detect COVID-19 pneumonia from CXR images. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical practice. The existing explanation methods produce either too noisy or imprecise results, and hence are unsuitable for diagnostic purposes. In this work, we propose a novel explainable CXR deep neural Network (CXR-Net) for accurate COVID-19 pneumonia detection with an enhanced pixel-level visual explanation using CXR images. An Encoder-Decoder-Encoder architecture is proposed, in which an extra encoder is added after the encoder-decoder structure to ensure the model can be trained on category samples. The method has been evaluated on real world CXR datasets from both public and private sources, including healthy, bacterial pneumonia, viral pneumonia and COVID-19 pneumonia cases. The results demonstrate that the proposed method can achieve a satisfactory accuracy and provide fine-resolution activation maps for visual explanation in the lung disease detection. Compared to current state-of-the-art visual explanation methods, the proposed method can provide more detailed, high-resolution, visual explanation for the classification results. It can be deployed in various computing environments, including cloud, CPU and GPU environments. It has a great potential to be used in clinical practice for COVID-19 pneumonia diagnosis.

IEEE journal of biomedical and health informatics

10.1109/JBHI.2022.3220813

Strong semantic segmentation for Covid-19 detection: Evaluating the use of deep learning models as a performant tool in radiography.

With the increasing number of Covid-19 cases as well as care costs, chest diseases have gained increasing interest in several communities, particularly in medical and computer vision. Clinical and analytical exams are widely recognized techniques for diagnosing and handling Covid-19 cases. However, strong detection tools can help avoid damage to chest tissues. The proposed method provides an important way to enhance the semantic segmentation process using combined potential deep learning (DL) modules to increase consistency. Based on Covid-19 CT images, this work hypothesized that a novel model for semantic segmentation might be able to extract definite graphical features of Covid-19 and afford an accurate clinical diagnosis while optimizing the classical test and saving time. CT images were collected considering different cases (normal chest CT, pneumonia, typical viral causes, and Covid-19 cases). The study presents an advanced DL method to deal with chest semantic segmentation issues. The approach employs a modified version of the U-net to enable and support Covid-19 detection from the studied images. The validation tests demonstrated competitive results with important performance rates: Precision (90.96% ± 2.5) with an F-score of (91.08% ± 3.2), an accuracy of (93.37% ± 1.2), a sensitivity of (96.88% ± 2.8) and a specificity of (96.91% ± 2.3). In addition, the visual segmentation results are very close to the Ground truth. The findings of this study reveal the proof-of-principle for using cooperative components to strengthen the semantic segmentation modules for effective and truthful Covid-19 diagnosis. This paper has highlighted that DL based approach, with several modules, may be contributing to provide strong support for radiographers and physicians, and that further use of DL is required to design and implement performant automated vision systems to detect chest diseases.

Radiography (London, England : 1995)

10.1016/j.radi.2022.10.010 10.1016/j.neucom.2017.08.043 10.3389/fnins.2018.00777 10.1007/s13369-021-05958-0 10.3390/technologies10050105 10.1109/ISBI.2016.7493515 10.5114/pjr.2022.119027 10.1109/CISP-BMEI.2018.8633056 10.1016/j.asoc.2021.107160 10.48550/arXiv.2110.09619 10.7937/K9/TCIA.2017.3r3fvz08 10.5281/zenodo.375747 10.1111/cgf.14521

Contrastive domain adaptation with consistency match for automated pneumonia diagnosis.

Pneumonia can be difficult to diagnose since its symptoms are too variable, and the radiographic signs are often very similar to those seen in other illnesses such as a cold or influenza. Deep neural networks have shown promising performance in automated pneumonia diagnosis using chest X-ray radiography, allowing mass screening and early intervention to reduce the severe cases and death toll. However, they usually require many well-labelled chest X-ray images for training to achieve high diagnostic accuracy. To reduce the need for training data and annotation resources, we propose a novel method called Contrastive Domain Adaptation with Consistency Match (CDACM). It transfers the knowledge from different but relevant datasets to the unlabelled small-size target dataset and improves the semantic quality of the learnt representations. Specifically, we design a conditional domain adversarial network to exploit discriminative information conveyed in the predictions to mitigate the domain gap between the source and target datasets. Furthermore, due to the small scale of the target dataset, we construct a feature cloud for each target sample and leverage contrastive learning to extract more discriminative features. Lastly, we propose adaptive feature cloud expansion to push the decision boundary to a low-density area. Unlike most existing transfer learning methods that aim only to mitigate the domain gap, our method instead simultaneously considers the domain gap and the data deficiency problem of the target dataset. The conditional domain adaptation and the feature cloud generation of our method are learning jointly to extract discriminative features in an end-to-end manner. Besides, the adaptive feature cloud expansion improves the model's generalisation ability in the target domain. Extensive experiments on pneumonia and COVID-19 diagnosis tasks demonstrate that our method outperforms several state-of-the-art unsupervised domain adaptation approaches, which verifies the effectiveness of CDACM for automated pneumonia diagnosis using chest X-ray imaging.

Medical image analysis

10.1016/j.media.2022.102664

Assessment of COVID-19 lung involvement on computed tomography by deep-learning-, threshold-, and human reader-based approaches-an international, multi-center comparative study.

The extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia, quantified on computed tomography (CT), is an established biomarker for prognosis and guides clinical decision-making. The clinical standard is semi-quantitative scoring of lung involvement by an experienced reader. We aim to compare the performance of automated deep-learning- and threshold-based methods to the manual semi-quantitative lung scoring. Further, we aim to investigate an optimal threshold for quantification of involved lung in COVID pneumonia chest CT, using a multi-center dataset. In total 250 patients were included, 50 consecutive patients with RT-PCR confirmed COVID-19 from our local institutional database, and another 200 patients from four international datasets (n=50 each). Lung involvement was scored semi-quantitatively by three experienced radiologists according to the established chest CT score (CCS) ranging from 0-25. Inter-rater reliability was reported by the intraclass correlation coefficient (ICC). Deep-learning-based segmentation of ground-glass and consolidation was obtained by CT Pulmo Auto Results prototype plugin on IntelliSpace Discovery (Philips Healthcare, The Netherlands). Threshold-based segmentation of involved lung was implemented using an open-source tool for whole-lung segmentation under the presence of severe pathologies (R231CovidWeb, Hofmanninger Median CCS among 250 evaluated patients was 10 [6-15]. Inter-rater reliability of the CCS was excellent [ICC 0.97 (0.97-0.98)]. Best attenuation threshold for identification of involved lung was -522 HU. While the relationship of deep-learning- and threshold-based quantification was linear and strong (r The manual semi-quantitative CCS underestimates the extent of COVID pneumonia in higher score ranges, which limits its clinical usefulness in cases of severe disease. Clinical implementation of fully automated methods, such as deep-learning or threshold-based approaches (best threshold in our multi-center dataset: -522 HU), might save time of trained personnel, abolish inter-reader variability, and allow for truly quantitative, linear assessment of COVID lung involvement.

Quantitative imaging in medicine and surgery

10.21037/qims-22-175 10.1148/rg.2020200159 10.1038/s41467-020-18786-x 10.3389/fpubh.2021.596938 10.1148/ryct.2020200130 10.1371/journal.pone.0237302 10.1186/s12931-020-01411-2 10.1007/s00330-020-07033-y 10.1186/s43055-021-00525-x 10.1148/ryct.2020200441 10.1148/radiol.2020200370 10.1148/ryai.2020200048 10.1007/s11760-022-02183-6 10.1148/radiol.2020201433 10.1007/s00330-020-07013-2 10.1148/radiol.2021203957 10.1186/s13104-021-05592-x 10.3390/bioengineering8020026 10.1007/s10278-013-9622-7 10.1007/s00330-021-08482-9 10.1117/12.2293528 10.1186/s41747-020-00173-2 10.1038/s41586-020-2649-2 10.1038/s41598-021-01489-8 10.1177/1536867X0800800212 10.3758/s13423-016-1039-0 10.1353/bsp.2020.0011 10.21037/jtd.2017.08.17 10.1117/1.1631315 10.1155/2021/6697677 10.1007/s00330-021-08435-2

A novel deep learning-based method for COVID-19 pneumonia detection from CT images.

The sensitivity of RT-PCR in diagnosing COVID-19 is only 60-70%, and chest CT plays an indispensable role in the auxiliary diagnosis of COVID-19 pneumonia, but the results of CT imaging are highly dependent on professional radiologists. This study aimed to develop a deep learning model to assist radiologists in detecting COVID-19 pneumonia. The total study population was 437. The training dataset contained 26,477, 2468, and 8104 CT images of normal, CAP, and COVID-19, respectively. The validation dataset contained 14,076, 1028, and 3376 CT images of normal, CAP, and COVID-19 patients, respectively. The test set included 51 normal cases, 28 CAP patients, and 51 COVID-19 patients. We designed and trained a deep learning model to recognize normal, CAP, and COVID-19 patients based on U-Net and ResNet-50. Moreover, the diagnoses of the deep learning model were compared with different levels of radiologists. In the test set, the sensitivity of the deep learning model in diagnosing normal cases, CAP, and COVID-19 patients was 98.03%, 89.28%, and 92.15%, respectively. The diagnostic accuracy of the deep learning model was 93.84%. In the validation set, the accuracy was 92.86%, which was better than that of two novice doctors (86.73% and 87.75%) and almost equal to that of two experts (94.90% and 93.88%). The AI model performed significantly better than all four radiologists in terms of time consumption (35 min vs. 75 min, 93 min, 79 min, and 82 min). The AI model we obtained had strong decision-making ability, which could potentially assist doctors in detecting COVID-19 pneumonia.

BMC medical informatics and decision making

10.1186/s12911-022-02022-1 10.1016/S0140-6736(20)30211-7 10.1001/jama.2020.1585 10.1056/NEJMoa2001316 10.1056/NEJMoa2001191 10.1101/2020.02.11.20021493v2 10.1148/radiol.2020200432 10.1109/TMI.2020.2996645 10.1148/radiol.2020200343 10.1016/S0140-6736(20)30154-9 10.1148/ryct.2020204002 10.1148/radiol.2020200642 10.1038/s41467-020-17971-2 10.1016/j.cell.2020.08.029 10.1016/j.media.2021.102096 10.1007/s00354-022-00172-4 10.18280/ts.370313 10.1186/s41747-020-00173-2 10.1016/j.compbiomed.2020.103792 10.18280/ts.380117 10.1007/s10044-021-00984-y 10.21203/rs.3.rs-104621/v1 10.1148/radiol.2020200905 10.1001/jama.2020.8259 10.7326/M20-1495 10.1001/jama.2020.12839 10.1148/radiol.2020200702

Towards smart diagnostic methods for COVID-19: Review of deep learning for medical imaging.

The infectious disease known as COVID-19 has spread dramatically all over the world since December 2019. The fast diagnosis and isolation of infected patients are key factors in slowing down the spread of this virus and better management of the pandemic. Although the CT and X-ray modalities are commonly used for the diagnosis of COVID-19, identifying COVID-19 patients from medical images is a time-consuming and error-prone task. Artificial intelligence has shown to have great potential to speed up and optimize the prognosis and diagnosis process of COVID-19. Herein, we review publications on the application of deep learning (DL) techniques for diagnostics of patients with COVID-19 using CT and X-ray chest images for a period from January 2020 to October 2021. Our review focuses solely on peer-reviewed, well-documented articles. It provides a comprehensive summary of the technical details of models developed in these articles and discusses the challenges in the smart diagnosis of COVID-19 using DL techniques. Based on these challenges, it seems that the effectiveness of the developed models in clinical use needs to be further investigated. This review provides some recommendations to help researchers develop more accurate prediction models.

IPEM-translation

10.1016/j.ipemt.2022.100008 10.1038/s41591-020-0931-3 10.1007/s00330-020-06801-0 10.1016/j.tmaid.2020.101623 10.1148/radiol.2020200823 10.1007/s42600-021-00151-6 10.3390/ai1030027 10.3390/electronics8030292 10.1155/2021/8829829 10.22061/jecei.2022.8200.491 10.1038/s41598-021-99015-3 10.1109/UEMCON47517.2019.8993089 10.1186/s40537-021-00444-8 10.1186/s13634-021-00755-1 10.1016/j.media.2021.102253 10.1148/radiol.2020201491 10.1109/ACCESS.2021.3086020 10.3390/electronics11152296 10.1109/ACCESS.2020.2994762 10.1109/TMI.2020.2994459 10.1007/s40477-020-00458-7 10.1148/radiol.2020200490 10.1007/s10489-020-01900-3 10.1021/ci0342472 10.1109/IACC.2016.25 10.1016/S0031-3203(02)00121-8 10.1109/TKDE.2009.191 10.4018/978-1-60566-766-9.ch011 10.1016/j.cmpb.2020.105608 10.1108/IJPCC-06-2020-0060 10.1007/978-3-540-75171-7_2 10.1007/978-3-540-28650-9_5 10.1093/nsr/nwx106 10.1109/TMI.2020.2994908 10.1109/TMI.2020.3000314 10.1109/TMI.2020.2996645 10.1038/s41746-021-00399-3 10.1109/ACCESS.2020.3003810 10.3390/app10165683 10.1080/07391102.2020.1767212 10.1148/radiol.2020200905 10.1016/j.compbiomed.2020.103869 10.1038/nbt1206-1565 10.1007/s12010-021-03728-0 10.7326/M20-1495 10.1016/j.bea.2021.100003 10.1148/radiol.2020200432 10.1016/j.clinimag.2021.02.003 10.1148/radiol.2020200370 10.1148/radiol.2020200463 10.1148/rg.2020200159 10.1016/j.clinimag.2020.04.001 10.1016/j.chaos.2020.109947 10.1186/s12890-020-01286-5 10.1016/j.chaos.2020.109944 10.1016/j.compbiomed.2020.103805 10.1007/s10489-020-01829-7 10.1016/j.mehy.2020.109761 10.1016/j.compbiomed.2020.103792 10.1016/j.cmpb.2020.105532 10.1016/j.cmpb.2020.105581 10.1007/s40846-020-00529-4 10.3390/sym12040651 10.1007/s13246-020-00865-4 10.1016/j.imu.2020.100360 10.1007/s00264-020-04609-7 10.1007/s13246-020-00888-x 10.1109/TMI.2020.2993291 10.1016/j.imu.2020.100405 10.3389/fmed.2020.00427 10.3892/etm.2020.8797 10.1016/j.asoc.2020.106580 10.1007/s10489-020-01867-1 10.1016/j.radi.2020.10.018 10.1007/s00521-020-05636-6 10.1016/j.bbe.2020.08.008 10.1016/j.chaos.2020.110245 10.1007/s12652-020-02688-3 10.1016/j.compbiomed.2021.104252 10.1007/s11571-021-09712-y 10.1016/j.compbiomed.2021.104927 10.1007/s10489-020-01714-3 10.1080/07391102.2020.1788642 10.1016/j.ejrad.2020.109041 10.1038/s41467-020-17971-2 10.1016/j.compbiomed.2020.103795 10.1109/TMI.2020.2995508 10.21037/atm.2020.03.132 10.1109/TMI.2020.2996256 10.3390/e22050517 10.1007/s00330-020-06956-w 10.1016/j.irbm.2020.05.003 10.1007/s10096-020-03901-z 10.1007/s00259-020-04929-1 10.1109/TMI.2020.2995108 10.1109/ACCESS.2020.3005510 10.1183/13993003.00775-2020 10.2196/19569 10.1007/s00330-021-07715-1 10.1038/s41598-020-76282-0 10.1109/TCBB.2021.3065361 10.1016/j.compbiomed.2021.104306 10.1016/j.compbiomed.2021.104837 10.1016/j.chaos.2021.111310 10.1016/j.patcog.2021.107826 10.1007/s00521-020-05410-8 10.1007/s10916-018-1088-1 10.34133/2021/8786793 10.4018/978-1-7998-8929-8.ch001 10.1007/s00530-021-00794-6 10.1109/ICIRCA48905.2020.9183278 10.1007/s10462-020-09825-6 10.1002/acm2.13121 10.1007/s00521-021-06344-5 10.1002/mp.15419 10.1038/s41598-021-88807-2 10.2196/19673 10.1109/TIP.2021.3058783 10.1097/MCP.0000000000000765 10.3390/diagnostics11071155 10.1016/j.ultrasmedbio.2020.07.003 10.1002/int.22504 10.1016/j.acra.2020.04.032 10.1016/j.mri.2021.03.005 10.1117/1.JBO.19.1.010901 10.1021/acsnano.1c05226 10.3389/fmicb.2020.02014 10.1016/B978-0-08-100040-3.00002-X 10.1016/j.asoc.2021.107150 10.1016/j.compbiomed.2020.104037 10.1101/2020.03.19.20039354 10.1177/0846537120913033 10.1186/s13244-020-00933-z 10.1016/j.ijid.2020.06.026 10.1016/j.diii.2020.03.014 10.1093/cid/ciaa247 10.1007/s10462-021-09975-1 10.1007/978-1-4419-9326-7_1 10.1109/ICC40277.2020.9148817 10.1148/radiol.2020200330

A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications.

The Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single country but the entire planet due to this infectious disease. Illnesses of Covid-19 spread at a much faster rate than usual influenza cases. Because of its high transmissibility and early diagnosis, it isn't easy to manage COVID-19. The popularly used RT-PCR method for COVID-19 disease diagnosis may provide false negatives. COVID-19 can be detected non-invasively using medical imaging procedures such as chest CT and chest x-ray. Deep learning is the most effective machine learning approach for examining a considerable quantity of chest computed tomography (CT) pictures that can significantly affect Covid-19 screening. Convolutional neural network (CNN) is one of the most popular deep learning techniques right now, and its gaining traction due to its potential to transform several spheres of human life. This research aims to develop conceptual transfer learning enhanced CNN framework models for detecting COVID-19 with CT scan images. Though with minimal datasets, these techniques were demonstrated to be effective in detecting the presence of COVID-19. This proposed research looks into several deep transfer learning-based CNN approaches for detecting the presence of COVID-19 in chest CT images.VGG16, VGG19, Densenet121, InceptionV3, Xception, and Resnet50 are the foundation models used in this work. Each model's performance was evaluated using a confusion matrix and various performance measures such as accuracy, recall, precision, f1-score, loss, and ROC. The VGG16 model performed much better than the other models in this study (98.00 % accuracy). Promising outcomes from experiments have revealed the merits of the proposed model for detecting and monitoring COVID-19 patients. This could help practitioners and academics create a tool to help minimal health professionals decide on the best course of therapy.

Advances in engineering software (Barking, London, England : 1992)

10.1016/j.advengsoft.2022.103317

A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis.

This paper aims to diagnose COVID-19 by using Chest X-Ray (CXR) scan images in a deep learning-based system. First of all, COVID-19 Chest X-Ray Dataset is used to segment the lung parts in CXR images semantically. DeepLabV3+ architecture is trained by using the masks of the lung parts in this dataset. The trained architecture is then fed with images in the COVID-19 Radiography Database. In order to improve the output images, some image preprocessing steps are applied. As a result, lung regions are successfully segmented from CXR images. The next step is feature extraction and classification. While features are extracted with modified AlexNet (mAlexNet), Support Vector Machine (SVM) is used for classification. As a result, 3-class data consisting of Normal, Viral Pneumonia and COVID-19 class are classified with 99.8% success. Classification results show that the proposed method is superior to previous state-of-the-art methods.

Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society

10.1016/j.chemolab.2022.104695 10.1109/RBME.2020.2987975 10.1007/s11760-022-02302-3 10.1016/j.eng.2020.04.010

Deep-learning-based hepatic fat assessment (DeHFt) on non-contrast chest CT and its association with disease severity in COVID-19 infections: A multi-site retrospective study.

Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (b) investigating the association between HS (liver-to-spleen attenuation ratio <1 in CT) and COVID-19 infections severity, wherein severity is defined as requiring invasive mechanical ventilation, extracorporeal membrane oxygenation, death. DeHFt comprises two steps. First, a deep-learning-based segmentation model (3D residual-UNet) is trained (N.ß=.ß80) to segment the liver and spleen. Second, CT attenuation is estimated using slice-based and volumetric-based methods. DeHFt-based mean liver and liver-to-spleen attenuation are compared with an expert's ROI-based measurements. We further obtained the liver-to-spleen attenuation ratio in a large multi-site cohort of patients with COVID-19 infections (D1, N.ß=.ß805; D2, N.ß=.ß1917; D3, N.ß=.ß169) using the DeHFt pipeline and investigated the association between HS and COVID-19 infections severity. The DeHFt pipeline achieved a dice coefficient of 0.95, 95% CI [0.93...0.96] on the independent validation cohort (N.ß=.ß49). The automated slice-based and volumetric-based liver and liver-to-spleen attenuation estimations strongly correlated with expert's measurement. In the COVID-19 cohorts, severe infections had a higher proportion of patients with HS than non-severe infections (pooled OR.ß=.ß1.50, 95% CI [1.20...1.88], P.ß<.ß.001). The DeHFt pipeline enabled accurate segmentation of liver and spleen on non-contrast CTs and automated estimation of liver and liver-to-spleen attenuation ratio. In three cohorts of patients with COVID-19 infections (N.ß=.ß2891), HS was associated with disease severity. Pending validation, DeHFt provides an automated CT-based metabolic risk assessment. For a full list of funding bodies, please see the Acknowledgements.

EBioMedicine

10.1016/j.ebiom.2022.104315 10.1016/j.acra.2012.02.022 10.48550/arXiv.1901.04056

Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey.

Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects. It is observed that COVID-19 has blown out globally so much in a short period because of the shortage of testing facilities and delays in test reports. To address this challenge, AI can be effectively applied to produce fast as well as cost-effective solutions. Plenty of researchers come up with AI-based solutions for preliminary diagnosis using chest CT Images, respiratory sound analysis, voice analysis of symptomatic persons with asymptomatic ones, and so forth. Some AI-based applications claim good accuracy in predicting the chances of being COVID-19-positive. Within a short period, plenty of research work is published regarding the identification of COVID-19. This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv. Most of the papers selected for this survey presented candid work to detect and classify COVID-19, using deep-learning-based models from chest X-Rays and CT scan images. We hope that this survey covers most of the work and provides insights to the research community in proposing efficient as well as accurate solutions for fighting the pandemic.

Contrast media & molecular imaging

10.1155/2022/1306664 10.1016/j.jbi.2008.05.013 10.1038/s41591-020-0931-3 10.1016/j.asoc.2020.106282 10.1016/j.chaos.2020.110055 10.1007/s42979-021-00923-y 10.3390/ai1020009 10.1016/j.chaos.2020.110059 10.1016/S2214-109X(20)30068-1 10.1016/j.clinimag.2020.02.008 10.1148/ryct.2020200082 10.1007/978-3-030-00889-5_1 10.1101/2020.03.12.20027185 10.1109/TCBB.2021.3065361 10.1016/j.cell.2020.04.045 10.1155/2022/7377502 10.1148/radiol.2020200905 10.1016/j.compbiomed.2020.103795 10.1038/s41598-020-76282-0 10.1007/s00330-021-07715-1 10.1016/j.irbm.2020.05.003 10.1007/s00330-020-06713-z 10.1016/j.ejrad.2020.109041 10.1109/RBME.2020.2987975 10.12669/pjms.36.covid19-s4.2778 10.1155/2022/8549707 10.3390/ijerph18063056 10.3390/healthcare9050522 10.1016/j.chaos.2020.109944 10.1155/2019/4180949 10.1038/s41598-020-76550-z 10.37896/jxu14.8/061 10.1007/s10489-020-01867-1 10.1007/s40846-020-00529-4 10.1109/CVPR.2018.00474 10.1016/j.compbiomed.2020.103805 10.1016/j.compbiomed.2020.103792 10.1007/s10044-021-00984-y 10.1109/CVPR.2015.7298594 10.1089/pop.2014.0089 10.33889/ijmems.2020.5.4.052 10.1109/CVPR.2017.243 10.1016/j.cmpb.2020.105581 10.1016/j.imu.2020.100360 10.1109/ACCESS.2020.3010287 10.1371/journal.pmed.1002686 10.1007/s13246-020-00865-4 10.1016/j.chaos.2020.110071 10.1016/j.cmpb.2020.105608 10.1016/j.mehy.2020.109761 10.1016/j.compbiomed.2020.103869 10.1016/j.cmpb.2020.105532 10.1007/s00330-020-07044-9 10.1007/s10489-020-01826-w 10.1007/s10489-020-01831-z 10.1007/s00264-020-04609-7 10.1007/s00500-020-05275-y 10.1155/2022/9697285 10.1007/s42979-021-00823-1 10.1101/2020.04.02.20051136v1 10.1101/2020.04.02.20051136 10.1088/1361-6560/abe838 10.1007/s41870-022-00949-2 10.1101/2020.02.29.20029603 10.1101/2020.03.25.20043331 10.1101/2020.04.04.20052092 10.1016/j.chaos.2020.110086 10.1186/s12859-018-2340-x 10.3390/a13100249 10.1007/s41870-022-00967-0 10.1101/2020.07.02.20145474 10.1186/s12911-020-01266-z 10.1101/2020.05.20.20107847 10.1101/2020.06.25.20140004 10.1101/2020.06.01.20119560 10.1080/09720502.2020.1833443 10.1016/j.patter.2020.100145 10.1101/2020.05.23.20110189 10.1016/j.chaos.2020.110058 10.1016/j.iot.2020.100222 10.1016/j.dsx.2020.04.012 10.1007/s41870-022-00973-2 10.1016/j.dsx.2020.04.032 10.1016/S2589-7500(20)30059-5 10.1007/s42247-020-00102-4 10.1109/access.2020.2992341 10.1088/1757-899x/1099/1/012005 10.1007/s00259-020-04953-1 10.1007/s10489-020-01770-9 10.1007/s42979-020-00410-w 10.1007/s41870-020-00571-0 10.1101/2020.04.24.20078584

A CNN-transformer fusion network for COVID-19 CXR image classification.

The global health crisis due to the fast spread of coronavirus disease (Covid-19) has caused great danger to all aspects of healthcare, economy, and other aspects. The highly infectious and insidious nature of the new coronavirus greatly increases the difficulty of outbreak prevention and control. The early and rapid detection of Covid-19 is an effective way to reduce the spread of Covid-19. However, detecting Covid-19 accurately and quickly in large populations remains to be a major challenge worldwide. In this study, A CNN-transformer fusion framework is proposed for the automatic classification of pneumonia on chest X-ray. This framework includes two parts: data processing and image classification. The data processing stage is to eliminate the differences between data from different medical institutions so that they have the same storage format; in the image classification stage, we use a multi-branch network with a custom convolution module and a transformer module, including feature extraction, feature focus, and feature classification sub-networks. Feature extraction subnetworks extract the shallow features of the image and interact with the information through the convolution and transformer modules. Both the local and global features are extracted by the convolution module and transformer module of feature-focus subnetworks, and are classified by the feature classification subnetworks. The proposed network could decide whether or not a patient has pneumonia, and differentiate between Covid-19 and bacterial pneumonia. This network was implemented on the collected benchmark datasets and the result shows that accuracy, precision, recall, and F1 score are 97.09%, 97.16%, 96.93%, and 97.04%, respectively. Our network was compared with other researchers' proposed methods and achieved better results in terms of accuracy, precision, and F1 score, proving that it is superior for Covid-19 detection. With further improvements to this network, we hope that it will provide doctors with an effective tool for diagnosing Covid-19.

PloS one

10.1371/journal.pone.0276758 10.1155/2021/2560388 10.1001/jama.2020.1585 10.1148/radiol.2020200432 10.1155/2022/1465173 10.1002/mp.13264 10.1148/radiol.2020200463 10.1136/bmj.m1328 10.1016/j.media.2017.07.005 10.1109/TPAMI.2019.2913372 10.1016/j.compbiomed.2020.103792 10.3389/fmed.2020.00427/full 10.1016/j.media.2020.101794 10.1038/s41598-020-76550-z 10.3389/fpubh.2022.948205/full 10.3389/fpubh.2022.948205 10.1155/2022/4254631 10.1016/j.mlwa.2021.100138 10.1007/978-981-33-4673-4_55 10.1148/radiol.2021203957 10.1007/s10278-013-9622-7

Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning.

COVID-19 has infected millions of people worldwide over the past few years. The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise. X-ray imaging is an alternative and more accessible technique. This study aimed to improve detection accuracy to create a computer-aided diagnostic tool. Combining other artificial intelligence applications techniques with radiological imaging can help detect different diseases. This study proposes a technique for the automatic detection of COVID-19 and other chest-related diseases using digital chest X-ray images of suspected patients by applying transfer learning (TL) algorithms. For this purpose, two balanced datasets, Dataset-1 and Dataset-2, were created by combining four public databases and collecting images from recently published articles. Dataset-1 consisted of 6000 chest X-ray images with 1500 for each class. Dataset-2 consisted of 7200 images with 1200 for each class. To train and test the model, TL with nine pretrained convolutional neural networks (CNNs) was used with augmentation as a preprocessing method. The network was trained to classify using five classifiers: two-class classifier (normal and COVID-19); three-class classifier (normal, COVID-19, and viral pneumonia), four-class classifier (normal, viral pneumonia, COVID-19, and tuberculosis (Tb)), five-class classifier (normal, bacterial pneumonia, COVID-19, Tb, and pneumothorax), and six-class classifier (normal, bacterial pneumonia, COVID-19, viral pneumonia, Tb, and pneumothorax). For two, three, four, five, and six classes, our model achieved a maximum accuracy of 99.83, 98.11, 97.00, 94.66, and 87.29%, respectively.

Sensors (Basel, Switzerland)

10.3390/s22207977 10.1016/j.jaut.2020.102433 10.1109/ACCESS.2020.3010287 10.1109/ACCESS.2017.2788044 10.1155/2022/6112815 10.1007/s11042-019-07820-w 10.1109/JBHI.2015.2425041 10.1016/j.irbm.2020.05.003 10.1016/j.compbiomed.2020.103795 10.1007/s10916-021-01745-4 10.1007/s10044-021-00984-y 10.3390/sym12040651 10.1007/s42600-021-00151-6 10.1007/s13246-020-00865-4 10.1016/j.compbiomed.2020.103792 10.1007/s11263-015-0816-y 10.1016/j.chaos.2020.109944 10.1016/j.cmpb.2020.105581 10.1038/s41598-020-76550-z 10.1016/j.eswa.2020.114054 10.1007/s00264-020-04609-7 10.1155/2018/2908517 10.1016/j.cell.2018.02.010 10.1109/TMI.2013.2284099 10.1109/TMI.2013.2290491 10.1016/j.compbiomed.2019.04.024 10.1145/3065386 10.1186/s40537-019-0192-5 10.1016/j.compbiomed.2021.104608

Role of Drone Technology Helping in Alleviating the COVID-19 Pandemic.

The COVID-19 pandemic, caused by a new coronavirus, has affected economic and social standards as governments and healthcare regulatory agencies throughout the world expressed worry and explored harsh preventative measures to counteract the disease's spread and intensity. Several academics and experts are primarily concerned with halting the continuous spread of the unique virus. Social separation, the closing of borders, the avoidance of big gatherings, contactless transit, and quarantine are important methods. Multiple nations employ autonomous, digital, wireless, and other promising technologies to tackle this coronary pneumonia. This research examines a number of potential technologies, including unmanned aerial vehicles (UAVs), artificial intelligence (AI), blockchain, deep learning (DL), the Internet of Things (IoT), edge computing, and virtual reality (VR), in an effort to mitigate the danger of COVID-19. Due to their ability to transport food and medical supplies to a specific location, UAVs are currently being utilized as an innovative method to combat this illness. This research intends to examine the possibilities of UAVs in the context of the COVID-19 pandemic from several angles. UAVs offer intriguing options for delivering medical supplies, spraying disinfectants, broadcasting communications, conducting surveillance, inspecting, and screening patients for infection. This article examines the use of drones in healthcare as well as the advantages and disadvantages of strict adoption. Finally, challenges, opportunities, and future work are discussed to assist in adopting drone technology to tackle COVID-19-like diseases.

Micromachines

10.3390/mi13101593 10.1109/IOTM.0011.2100053 10.1017/dmp.2021.9 10.3390/drones6010015 10.1007/s10796-021-10131-x 10.3389/frcmn.2020.566853 10.1080/14649365.2021.1921245 10.1177/2043820620934267 10.1016/j.future.2020.08.046 10.1016/j.retram.2020.01.002 10.2139/ssrn.3565463 10.1109/ACCESS.2018.2875739 10.1109/ACCESS.2019.2905347 10.3390/drones4040068 10.1109/TMM.2021.3075566 10.1109/TITS.2021.3113787 10.1109/TII.2022.3174160 10.3389/fpubh.2022.855994 10.1007/s11655-020-3192-6 10.1038/s41598-020-73510-5 10.1002/er.6007 10.3390/drones6060147 10.3390/rs11070820 10.3390/rs12213539 10.3390/mi13060977 10.1016/j.iot.2020.100218 10.1145/3001836 10.1016/j.adhoc.2020.102324 10.1080/17538947.2021.1952324 10.1049/ntw2.12040 10.3390/drones5030058 10.1002/ett.4255 10.3390/drones5010018 10.3390/drones6050109 10.3390/ijerph18052637 10.53553/JCH.v09i01.009 10.1016/j.trip.2021.100453 10.1016/j.ijhm.2020.102758 10.1016/j.ajic.2022.03.004 10.1016/j.vaccine.2016.06.022 10.3390/ijerph17239117 10.28991/HIJ-2020-01-02-03 10.1007/s13205-020-02581-y 10.1155/2022/9718580 10.1109/MNET.011.2000439 10.1007/s10462-021-10106-z 10.1007/s41666-020-00080-6 10.1016/j.ceh.2020.03.001 10.4108/eai.13-7-2018.163997 10.1016/j.clineuro.2021.106655 10.1109/MCE.2020.2992034 10.1101/2020.04.06.20039909 10.1002/ett.4245 10.1108/IJPCC-05-2020-0046 10.1016/j.scs.2020.102589 10.1136/bmjsem-2020-000943 10.1016/j.jnca.2022.103341 10.1109/TII.2021.3101651 10.1007/s10586-022-03722-z 10.1016/j.ijhcs.2020.102573 10.1007/s43926-021-00005-8 10.1016/j.bspc.2022.103658 10.3390/app12062828 10.3389/fnbot.2022.840594 10.1016/j.techfore.2020.120431 10.32604/cmc.2022.021850 10.1007/s00607-021-01022-9 10.1186/s12909-020-02245-8 10.3389/fneur.2021.646902 10.1007/s12311-020-01139-1 10.1002/adma.202103646 10.1016/j.jnlssr.2020.06.011 10.1109/JBHI.2021.3103404 10.1109/IOTM.1100.2000068 10.1109/MWC.001.2000429 10.1109/JSEN.2022.3188929 10.1109/ACCESS.2021.3133796 10.1007/s00607-022-01064-7 10.3991/ijim.v15i22.22623 10.1007/s11036-018-1193-x 10.3390/drones4040065 10.1016/j.jnca.2016.12.012 10.1109/JIOT.2020.3007518 10.1111/1758-5899.13007

Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review.

Machine-learning (ML) and deep-learning (DL) algorithms are part of a group of modeling algorithms that grasp the hidden patterns in data based on a training process, enabling them to extract complex information from the input data. In the past decade, these algorithms have been increasingly used for image processing, specifically in the medical domain. Cardiothoracic imaging is one of the early adopters of ML/DL research, and the COVID-19 pandemic resulted in more research focus on the feasibility and applications of ML/DL in cardiothoracic imaging. In this scoping review, we systematically searched available peer-reviewed medical literature on cardiothoracic imaging and quantitatively extracted key data elements in order to get a big picture of how ML/DL have been used in the rapidly evolving cardiothoracic imaging field. During this report, we provide insights on different applications of ML/DL and some nuances pertaining to this specific field of research. Finally, we provide general suggestions on how researchers can make their research more than just a proof-of-concept and move toward clinical adoption.

Diagnostics (Basel, Switzerland)

10.3390/diagnostics12102512 10.1007/s12525-021-00475-2 10.4997/jrcpe.2020.309 10.1093/eurheartj/ehw302 10.3389/fdata.2018.00006 10.3348/kjr.2017.18.4.570 10.1001/jama.2018.1150 10.1007/s00256-021-03876-8 10.1038/s42256-021-00305-2 10.1016/j.acra.2021.12.032 10.1007/s00330-021-07781-5 10.11622/smedj.2019141 10.1016/j.jacr.2017.12.021 10.1097/RTI.0000000000000453 10.1038/s41598-021-84698-5 10.1016/j.acra.2018.10.007 10.1259/bjro.20200037 10.1007/s00247-021-05177-7 10.1016/j.media.2017.06.015 10.1007/978-981-16-3783-4_15 10.7326/M18-0850 10.1007/s10462-021-10106-z 10.1145/3065386 10.5555/1593511 10.1186/s12874-021-01404-9 10.1007/s00330-020-07450-z 10.1148/radiol.2020200432 10.1148/radiol.2020204226 10.1016/j.compbiomed.2021.104304 10.1148/ryai.2019180041 10.1118/1.3528204 10.1016/j.cmpb.2021.106373 10.3348/kjr.2021.0148 10.3390/s21217059 10.1007/s00330-019-06628-4 10.1609/aaai.v33i01.3301590 10.1186/s41747-018-0068-z 10.1186/s13244-020-00887-2 10.1148/ryai.220010 10.1016/j.cmpb.2019.05.020 10.1016/j.compbiomed.2022.105466 10.1007/s00431-021-04061-8 10.1155/2021/6050433 10.1016/j.cmpb.2022.106815 10.1155/2022/4185835 10.1016/j.ejmp.2019.11.026 10.1002/mp.15019 10.3390/tomography7040054 10.1055/a-1717-2703 10.1016/j.cmpb.2019.105288 10.1016/j.media.2020.101823 10.1016/j.compbiomed.2021.104689 10.1109/TMI.2018.2833385 10.1016/j.artmed.2020.101975 10.1016/j.morpho.2019.09.002 10.1002/mp.14066 10.1016/j.media.2022.102362 10.1109/TMI.2021.3053008 10.1088/1361-6560/ab1cee 10.1371/journal.pone.0244745 10.1016/j.media.2022.102491 10.1088/1361-6560/ab18db 10.3233/XST-17358 10.1148/ryai.220061 10.1148/radiol.2018182294 10.1148/rg.2020200040 10.1016/j.ejmp.2021.08.011 10.1016/j.lungcan.2021.01.027 10.1016/j.media.2022.102389 10.1097/RLI.0000000000000707 10.1148/ryai.2020190043 10.1148/ryai.2021200267 10.1007/s00530-022-00960-4 10.1148/ryai.210290 10.1016/j.acra.2021.09.007 10.1097/RLI.0000000000000763 10.1145/3531146.3533193

Pseudo-Label Guided Image Synthesis for Semi-Supervised COVID-19 Pneumonia Infection Segmentation.

Coronavirus disease 2019 (COVID-19) has become a severe global pandemic. Accurate pneumonia infection segmentation is important for assisting doctors in diagnosing COVID-19. Deep learning-based methods can be developed for automatic segmentation, but the lack of large-scale well-annotated COVID-19 training datasets may hinder their performance. Semi-supervised segmentation is a promising solution which explores large amounts of unlabelled data, while most existing methods focus on pseudo-label refinement. In this paper, we propose a new perspective on semi-supervised learning for COVID-19 pneumonia infection segmentation, namely pseudo-label guided image synthesis. The main idea is to keep the pseudo-labels and synthesize new images to match them. The synthetic image has the same COVID-19 infected regions as indicated in the pseudo-label, and the reference style extracted from the style code pool is added to make it more realistic. We introduce two representative methods by incorporating the synthetic images into model training, including single-stage Synthesis-Assisted Cross Pseudo Supervision (SA-CPS) and multi-stage Synthesis-Assisted Self-Training (SA-ST), which can work individually as well as cooperatively. Synthesis-assisted methods expand the training data with high-quality synthetic data, thus improving the segmentation performance. Extensive experiments on two COVID-19 CT datasets for segmenting the infections demonstrate our method is superior to existing schemes for semi-supervised segmentation, and achieves the state-of-the-art performance on both datasets. Code is available at: https://github.com/FeiLyu/SASSL.

IEEE transactions on medical imaging

10.1109/TMI.2022.3217501

Comprehensive Survey of Machine Learning Systems for COVID-19 Detection.

The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.

Journal of imaging

10.3390/jimaging8100267 10.1177/2347631120983481 10.1016/j.jbusres.2020.05.030 10.1080/16549716.2020.1788263 10.11591/ijece.v10i5.pp4738-4744 10.1148/rg.2017160130 10.1016/j.jmir.2019.09.005 10.1147/rd.33.0210 10.1109/MCE.2016.2640698 10.25046/aj030435 10.1007/s10514-022-10039-8 10.1016/j.crad.2018.05.015 10.1038/nature14539 10.1021/acs.chas.0c00075 10.1561/2000000039 10.1016/j.neunet.2014.09.003 10.1109/TSMC.1971.4308320 10.1109/5.726791 10.1016/j.knosys.2018.10.034 10.1145/2347736.2347755 10.1016/j.jinf.2020.04.004 10.1016/j.imu.2020.100449 10.1038/s41598-021-93719-2 10.3390/s22062224 10.1093/clinchem/hvaa200 10.1016/j.chaos.2020.110120 10.1007/s10462-021-10106-z 10.1016/j.eswa.2022.116540 10.34306/ajri.v3i2.659 10.1111/exsy.12759 10.1007/s11042-020-09894-3 10.1109/TIP.2021.3058783 10.1109/JBHI.2021.3074893 10.1007/s12559-020-09785-7 10.1007/s13246-020-00865-4 10.1109/ACCESS.2020.3010287 10.1016/j.cmpb.2020.105608 10.1109/JBHI.2020.3037127 10.1016/j.patrec.2020.09.010 10.1007/s11042-021-10783-6 10.3389/fmed.2020.00427 10.1007/s10489-020-01826-w 10.1038/s41598-020-74539-2 10.1109/ACCESS.2020.2994762 10.1145/3422622 10.1016/j.compbiomed.2012.12.004 10.1002/mp.14676 10.1109/TMI.2020.2996645 10.1016/j.patcog.2021.108109 10.1155/2021/5544742 10.3390/s21217116 10.1016/j.compbiomed.2020.103869 10.1016/j.compbiomed.2020.104066 10.1016/j.compbiomed.2021.104348 10.1016/j.compbiomed.2021.104781 10.1016/j.chaos.2020.110170 10.1016/j.eswa.2020.114054 10.1016/j.aej.2021.06.024 10.1016/j.compbiomed.2021.104572 10.1016/j.imu.2020.100412 10.20944/preprints202003.0300.v1 10.1007/s11042-021-11299-9 10.1101/2020.04.11.20054643 10.1016/j.asoc.2021.108190 10.1101/2020.05.10.20097063 10.1101/2020.07.02.20136721 10.1016/j.imu.2022.100945 10.1101/2020.11.08.20228080 10.1016/j.compmedimag.2021.102008 10.1007/s40846-020-00529-4 10.1101/2020.03.30.20047787 10.1080/07391102.2020.1788642 10.1080/07391102.2021.1875049 10.1109/TII.2021.3057524 10.1016/j.radi.2020.10.018 10.1016/j.imu.2020.100505 10.1016/j.inffus.2021.04.008 10.1007/s42600-020-00091-7 10.1007/s10489-020-02010-w 10.3390/ijerph18158052 10.1002/ima.22525 10.1002/ima.22527 10.31661/jbpe.v0i0.2008-1153 10.1002/jemt.23713 10.1371/journal.pone.0242899 10.3390/jpm11010028 10.1007/s40747-020-00199-4 10.1007/s11760-020-01820-2 10.1016/j.patcog.2021.107848 10.1016/j.compbiomed.2020.104037 10.1007/s10044-021-00984-y 10.1152/physiolgenomics.00084.2020 10.14299/ijser.2020.03.02 10.20944/preprints202009.0524.v1 10.1148/radiol.2020200905 10.1007/s12539-020-00403-6 10.1007/s00530-021-00826-1 10.1186/s12938-020-00807-x 10.1186/s12938-020-00831-x 10.1038/s41598-020-76141-y 10.1007/s40747-020-00216-6 10.1007/s10096-020-03901-z 10.1007/s10489-020-02149-6 10.1007/s00259-020-04929-1 10.1007/s00521-021-06219-9 10.3390/sym12040651 10.1109/JAS.2020.1003393 10.1016/j.patcog.2020.107747 10.1109/ACCESS.2021.3058854 10.1016/j.bbe.2021.01.002 10.18517/ijaseit.10.2.11446 10.1109/TMI.2020.2995965 10.1007/s10489-020-01829-7 10.1007/s42600-020-00110-7 10.1080/02664763.2020.1849057 10.1016/j.compbiomed.2020.103792 10.3390/bioengineering8020026 10.1016/j.asoc.2022.108966 10.1155/2022/7377502 10.3389/fdgth.2021.662343 10.1049/ipr2.12474

Deep Transfer Learning for COVID-19 Detection and Lesion Recognition Using Chest CT Images.

Starting from December 2019, the global pandemic of coronavirus disease 2019 (COVID-19) is continuously expanding and has caused several millions of deaths worldwide. Fast and accurate diagnostic methods for COVID-19 detection play a vital role in containing the plague. Chest computed tomography (CT) is one of the most commonly used diagnosis methods. However, a complete CT-scan has hundreds of slices, and it is time-consuming for radiologists to check each slice to diagnose COVID-19. This study introduces a novel method for fast and automated COVID-19 diagnosis using the chest CT scans. The proposed models are based on the state-of-the-art deep convolutional neural network (CNN) architecture, and a 2D global max pooling (globalMaxPool2D) layer is used to improve the performance. We compare the proposed models to the existing state-of-the-art deep learning models such as CNN based models and vision transformer (ViT) models. Based off of metric such as area under curve (AUC), sensitivity, specificity, accuracy, and false discovery rate (FDR), experimental results show that the proposed models outperform the previous methods, and the best model achieves an area under curve of 0.9744 and accuracy 94.12% on our test datasets. It is also shown that the accuracy is improved by around 1% by using the 2D global max pooling layer. Moreover, a heatmap method to highlight the lesion area on COVID-19 chest CT images is introduced in the paper. This heatmap method is helpful for a radiologist to identify the abnormal pattern of COVID-19 on chest CT images. In addition, we also developed a freely accessible online simulation software for automated COVID-19 detection using CT images. The proposed deep learning models and software tool can be used by radiologist to diagnose COVID-19 more accurately and efficiently.

Computational and mathematical methods in medicine

10.1155/2022/4509394 10.1016/S0140-6736(20)30211-7 10.1001/jama.2020.1585 10.1111/all.14238 10.1016/j.jhin.2020.03.001 10.1093/cid/ciaa344 10.1148/radiol.2020200642 10.1148/radiol.2020200432 10.1007/s00330-020-06975-7 10.1016/S1473-3099(20)30086-4 10.1007/s00330-020-06731-x 10.1021/acssensors.0c02042 10.1146/annurev-bioeng-071516-044442 10.1007/978-1-4471-4929-3 10.1155/2018/5157020 10.1016/S2213-2600(18)30286-8 10.1148/radiol.2020192154 10.1136/gutjnl-2017-314547 10.1093/mind/LIX.236.433 10.1162/089976602760128018 10.1155/2017/8314740 10.1109/FG.2017.42 10.1109/HealthCom.2017.8210843 10.1016/j.compbiomed.2021.104306 10.1016/j.compbiomed.2020.103795 10.1038/s41591-020-0931-3 10.1016/j.ejrad.2020.109402 10.1016/j.compbiomed.2021.104348 10.1016/j.imu.2020.100427 10.7717/peerj.10086 10.1016/j.patrec.2020.10.001 10.3390/healthcare10010085 10.1109/TENSYMP52854.2021.9550819 10.1007/978-1-4842-2766-4 10.1109/ICEngTechnol.2017.8308186 10.1145/3065386 10.1609/aaai.v31i1.11231 10.3390/s22082988 10.1007/s11263-019-01228-7

Diagnostic performance of corona virus disease 2019 chest computer tomography image recognition based on deep learning: Systematic review and meta-analysis.

To analyze the diagnosis performance of deep learning model used in corona virus disease 2019 (COVID-19) computer tomography(CT) chest scans. The included sample contains healthy people, confirmed COVID-19 patients and unconfirmed suspected patients with corresponding symptoms. PubMed, Web of Science, Wiley, China National Knowledge Infrastructure, WAN FANG DATA, and Cochrane Library were searched for articles. Three researchers independently screened the literature, extracted the data. Any differences will be resolved by consulting the third author to ensure that a highly reliable and useful research paper is produced. Data were extracted from the final articles, including: authors, country of study, study type, sample size, participant demographics, type and name of AI software, results (accuracy, sensitivity, specificity, ROC, and predictive values), other outcome(s) if applicable. Among the 3891 searched results, 32 articles describing 51,392 confirmed patients and 7686 non-infected individuals met the inclusion criteria. The pooled sensitivity, the pooled specificity, positive likelihood ratio, negative likelihood ratio and the pooled diagnostic odds ratio (OR) is 0.87(95%CI [confidence interval]: 0.85, 0.89), 0.85(95%CI: 0.82, 0.87), 6.7(95%CI: 5.7, 7.8), 0.14(95%CI: 0.12, 0.16), and 49(95%CI: 38, 65). Further, the AUROC (area under the receiver operating characteristic curve) is 0.94(95%CI: 0.91, 0.96). Secondary outcomes are specific sensitivity and specificity within subgroups defined by different models. Resnet has the best diagnostic performance, which has the highest sensitivity (0.91[95%CI: 0.87, 0.94]), specificity (0.90[95%CI: 0.86, 0.93]) and AUROC (0.96[95%CI: 0.94, 0.97]), according to the AUROC, we can get the rank Resnet > Densenet > VGG > Mobilenet > Inception > Effficient > Alexnet. Our study findings show that deep learning models have immense potential in accurately stratifying COVID-19 patients and in correctly differentiating them from patients with other types of pneumonia and normal patients. Implementation of deep learning-based tools can assist radiologists in correctly and quickly detecting COVID-19 and, consequently, in combating the COVID-19 pandemic.

Medicine

10.1097/MD.0000000000031346

Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database.

The spread of coronavirus has been challenging for the healthcare system's proper management and diagnosis during the rapid spread and control of the infection. Real-time reverse transcription-polymerase chain reaction (RT-PCR), though considered the standard testing measure, has low sensitivity and is time-consuming, which restricts the fast screening of individuals. Therefore, computer tomography (CT) is used to complement the traditional approaches and provide fast and effective screening over other diagnostic methods. This work aims to appraise the importance of chest CT findings of COVID-19 and post-COVID in the diagnosis and prognosis of infected patients and to explore the ways and means to integrate CT findings for the development of advanced Artificial Intelligence (AI) tool-based predictive diagnostic techniques. The retrospective study includes a 188 patient database with COVID-19 infection confirmed by RT-PCR testing, including post-COVID patients. Patients underwent chest high-resolution computer tomography (HRCT), where the images were evaluated for common COVID-19 findings and involvement of the lung and its lobes based on the coverage region. The radiological modalities analyzed in this study may help the researchers in generating a predictive model based on AI tools for further classification with a high degree of reliability. Mild to moderate ground glass opacities (GGO) with or without consolidation, crazy paving patterns, and halo signs were common COVID-19 related findings. A CT score is assigned to every patient based on the severity of lung lobe involvement. Typical multifocal, bilateral, and peripheral distributions of GGO are the main characteristics related to COVID-19 pneumonia. Chest HRCT can be considered a standard method for timely and efficient assessment of disease progression and management severity. With its fusion with AI tools, chest HRCT can be used as a one-stop platform for radiological investigation and automated diagnosis system.

Biomedical signal processing and control

10.1016/j.bspc.2022.104297 10.1101/2020.02 10.1101/2020.03.12.20027185

The Deep Learning-Based Framework for Automated Predicting COVID-19 Severity Score.

With the COVID-19 pandemic sweeping the globe, an increasing number of people are working on pandemic research, but there is less effort on predicting its severity. Diagnostic chest imaging is thought to be a quick and reliable way to identify the severity of COVID-19. We describe a deep learning method to automatically predict the severity score of patients by analyzing chest X-rays, with the goal of collaborating with doctors to create corresponding treatment measures for patients and can also be used to track disease change. Our model consists of a feature extraction phase and an outcome prediction phase. The feature extraction phase uses a DenseNet backbone network to extract 18 features related to lung diseases from CXRs; the outcome prediction phase, which employs the MLP regression model, selects several important features for prediction from the features extracted in the previous phase and demonstrates the effectiveness of our model by comparing it with several commonly used regression models. On a dataset of 2373 CXRs, our model predicts the geographic extent score with 1.02 MAE and the lung opacity score with 0.85 MAE.

Procedia computer science

10.1016/j.procs.2022.09.165

Automatic deep learning-based consolidation/collapse classification in lung ultrasound images for COVID-19 induced pneumonia.

Our automated deep learning-based approach identifies consolidation/collapse in LUS images to aid in the identification of late stages of COVID-19 induced pneumonia, where consolidation/collapse is one of the possible associated pathologies. A common challenge in training such models is that annotating each frame of an ultrasound video requires high labelling effort. This effort in practice becomes prohibitive for large ultrasound datasets. To understand the impact of various degrees of labelling precision, we compare labelling strategies to train fully supervised models (frame-based method, higher labelling effort) and inaccurately supervised models (video-based methods, lower labelling effort), both of which yield binary predictions for LUS videos on a frame-by-frame level. We moreover introduce a novel sampled quaternary method which randomly samples only 10% of the LUS video frames and subsequently assigns (ordinal) categorical labels to all frames in the video based on the fraction of positively annotated samples. This method outperformed the inaccurately supervised video-based method and more surprisingly, the supervised frame-based approach with respect to metrics such as precision-recall area under curve (PR-AUC) and F1 score, despite being a form of inaccurate learning. We argue that our video-based method is more robust with respect to label noise and mitigates overfitting in a manner similar to label smoothing. The algorithm was trained using a ten-fold cross validation, which resulted in a PR-AUC score of 73% and an accuracy of 89%. While the efficacy of our classifier using the sampled quaternary method significantly lowers the labelling effort, it must be verified on a larger consolidation/collapse dataset, our proposed classifier using the sampled quaternary video-based method is clinically comparable with trained experts' performance.

Scientific reports

10.1038/s41598-022-22196-y 10.1016/j.compbiomed.2021.104742 10.1186/s12931-020-01504-y 10.1016/j.hrtlng.2021.02.015 10.1007/s12630-020-01704-6 10.1111/1742-6723.13546 10.1186/s13089-018-0103-6 10.1016/j.crad.2020.05.001 10.1109/TMI.2021.3117246 10.1109/TUFFC.2021.3070696 10.1109/TMI.2020.2994459 10.1109/TUFFC.2022.3161716 10.1016/j.imu.2021.100687 10.1109/TUFFC.2020.3002249 10.1109/TUFFC.2020.3005512 10.1002/jum.16052 10.1038/s41598-021-90153-2 10.1016/j.ejmp.2021.02.023 10.1186/s13063-019-4003-2 10.1002/jum.15548 10.1097/00005382-199621000-00002 10.1093/nsr/nwx106 10.1118/1.3611983 10.2307/2095465 10.1016/j.ipl.2005.11.003 10.1371/journal.pone.0118432 10.1007/978-3-642-40994-3_29 10.11613/BM.2012.031

COVID19 Diagnosis Using Chest X-rays and Transfer Learning.

A pandemic of respiratory illnesses from a novel coronavirus known as Sars-CoV-2 has swept across the globe since December of 2019. This is calling upon the research community including medical imaging to provide effective tools for use in combating this virus. Research in biomedical imaging of viral patients is already very active with machine learning models being created for diagnosing Sars-CoV-2 infections in patients using CT scans and chest x-rays. We aim to build upon this research. Here we used a transfer-learning approach to develop models capable of diagnosing COVID19 from chest x-ray. For this work we compiled a dataset of 112120 negative images from the Chest X-Ray 14 and 2725 positive images from public repositories. We tested multiple models, including logistic regression and random forest and XGBoost with and without principal components analysis, using five-fold cross-validation to evaluate recall, precision, and f1-score. These models were compared to a pre-trained deep-learning model for evaluating chest x-rays called COVID-Net. Our best model was XGBoost with principal components with a recall, precision, and f1-score of 0.692, 0.960, 0.804 respectively. This model greatly outperformed COVID-Net which scored 0.987, 0.025, 0.048. This model, with its high precision and reasonable sensitivity, would be most useful as "rule-in" test for COVID19. Though it outperforms some chemical assays in sensitivity, this model should be studied in patients who would not ordinarily receive a chest x-ray before being used for screening. Life and Medical Sciences • Machine Learning • Artificial Intelligence. Jonathan Stubblefield, Jason Causey, Dakota Dale, Jake Qualls, Emily Bellis, Jennifer Fowler, Karl Walker and Xiuzhen Huang. 2022. COVID19 Diagnosis Using Chest X-Rays and Transfer Learning.

medRxiv : the preprint server for health sciences

10.1101/2022.10.09.22280877 10.1145/2939672.2939785 10.1109/CVPR.2017.369 10.1101/2020.02.25.20021568 10.1101/2020.04.11.20062091 10.3390/diagnostics10090669 10.7937/91ah-v663 10.1148/radiol.2021203957 10.1007/s10278-013-9622-7 10.1613/jair.953

Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques.

The objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM). Second objective is to implement machine learning classifiers such as Naïve bayes, bagging and Reptree to classify the images into two image classes namely COVID and non-COVID and to compare the performance of the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet with that of the proposed machine learning classifiers. Our dataset consists of 100 COVID and non-COVID images which are pre-processed and segmented with our proposed algorithm. Following the feature extraction process, three machine learning classifiers (Naive Bayes, Bagging, and REPTree) were used to classify the normal and covid patients. We had implemented the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet for comparing their performance with machine learning classifiers. In machine learning, the Naive Bayes classifier achieved the highest accuracy of 97%, whereas the ResNet50 CNN model attained the highest accuracy of 99%. Hence the deep learning networks outperformed well compared to the machine learning techniques in the classification of Covid-19 images.

Scientific reports

10.1038/s41598-022-20804-5 10.1016/j.ajem.2020.04.048 10.1126/scitranslmed.abc1931 10.1136/bmj.m1403 10.1002/jmv.25721 10.1080/14737159.2020.1757437 10.1148/radiol.2021204522 10.1136/bmjopen-2020-04294 10.1016/j.ibmed.2020.100013 10.1007/s10489-020-01902-1 10.1007/s13246-020-00865-4 10.1007/s10140-020-01886-y 10.1007/s10096-020-03901-z 10.1007/s10044-021-00984-y 10.1007/s40009-020-01009-8 10.1155/2022/5329014 10.1016/j.jnlest.2022.100161 10.1016/j.compbiomed.2020.104037 10.1038/s41598-021-95537-y 10.1016/j.media.2020.101794 10.1016/j.bspc.2020.102365 10.1016/j.compbiomed.2020.103795 10.1148/radiol.2020191145 10.1148/rg.2017160130 10.18201/ijisae.2019252786 10.30534/ijatcse/2020/221932020 10.1109/TMI.2018.2806309 10.1016/j.jocs.2018.11.008

Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT.

Supervised deep learning has become a standard approach to solving medical image segmentation tasks. However, serious difficulties in attaining pixel-level annotations for sufficiently large volumetric datasets in real-life applications have highlighted the critical need for alternative approaches, such as semi-supervised learning, where model training can leverage small expert-annotated datasets to enable learning from much larger datasets without laborious annotation. Most of the semi-supervised approaches combine expert annotations and machine-generated annotations with equal weights within deep model training, despite the latter annotations being relatively unreliable and likely to affect model optimization negatively. To overcome this, we propose an active learning approach that uses an example re-weighting strategy, where machine-annotated samples are weighted (i) based on the similarity of their gradient directions of descent to those of expert-annotated data, and (ii) based on the gradient magnitude of the last layer of the deep model. Specifically, we present an active learning strategy with a query function that enables the selection of reliable and more informative samples from machine-annotated batch data generated by a noisy teacher. When validated on clinical COVID-19 CT benchmark data, our method improved the performance of pneumonia infection segmentation compared to the state of the art.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

10.1016/j.compmedimag.2022.102127 10.7937/tcia.2020.gqry-nc81

Attention induction for a CT volume classification of COVID-19.

This study proposes a method to draw attention toward the specific radiological findings of coronavirus disease 2019 (COVID-19) in CT images, such as bilaterality of ground glass opacity (GGO) and/or consolidation, in order to improve the classification accuracy of input CT images. We propose an induction mask that combines a similarity and a bilateral mask. A similarity mask guides attention to regions with similar appearances, and a bilateral mask induces attention to the opposite side of the lung to capture bilaterally distributed lesions. An induction mask for pleural effusion is also proposed in this study. ResNet18 with nonlocal blocks was trained by minimizing the loss function defined by the induction mask. The four-class classification accuracy of the CT images of 1504 cases was 0.6443, where class 1 was the typical appearance of COVID-19 pneumonia, class 2 was the indeterminate appearance of COVID-19 pneumonia, class 3 was the atypical appearance of COVID-19 pneumonia, and class 4 was negative for pneumonia. The four classes were divided into two subgroups. The accuracy of COVID-19 and pneumonia classifications was evaluated, which were 0.8205 and 0.8604, respectively. The accuracy of the four-class and COVID-19 classifications improved when attention was paid to pleural effusion. The proposed attention induction method was effective for the classification of CT images of COVID-19 patients. Improvement of the classification accuracy of class 3 by focusing on features specific to the class remains a topic for future work.

International journal of computer assisted radiology and surgery

10.1007/s11548-022-02769-y 10.1148/radiol.2020200432 10.1148/radiol.2020200343 10.1148/radiol.2020200905 10.1016/j.compbiomed.2020.103795 10.1007/s11356-020-10133-3 10.1109/ACCESS.2020.3016780 10.1109/TMI.2020.2994908 10.1016/j.patrec.2021.06.021 10.1016/j.media.2012.08.002 10.1007/s11263-015-0816-y 10.1007/BF02295996

Multi-texture features and optimized DeepNet for COVID-19 detection using chest x-ray images.

The corona virus disease 2019 (COVID-19) pandemic has a severe influence on population health all over the world. Various methods are developed for detecting the COVID-19, but the process of diagnosing this problem from radiology and radiography images is one of the effective procedures for diagnosing the affected patients. Therefore, a robust and effective multi-local texture features (MLTF)-based feature extraction approach and Improved Weed Sea-based DeepNet (IWS-based DeepNet) approach is proposed for detecting the COVID-19 at an earlier stage. The developed IWS-based DeepNet is developed for detecting COVID-19to optimize the structure of the Deep Convolutional Neural Network (Deep CNN). The IWS is devised by incorporating the Improved Invasive Weed Optimization (IIWO) and Sea Lion Optimization (SLnO), respectively. The noises present in the input chest x-ray (CXR) image are discarded using Region of Interest (RoI) extraction by adaptive thresholding technique. For feature extraction, the proposed MLFT is newly developed by considering various texture features for extracting the best features. Finally, the COVID-19 detection is performed using the proposed IWS-based DeepNet. Furthermore, the proposed technique achieved effective performance in terms of True Positive Rate (TPR), True Negative Rate (TNR), and accuracy with the maximum values of 0.933%, 0.890%, and 0.919%.

Concurrency and computation : practice & experience

10.1002/cpe.7157 10.1007/s12559-021-09848-3 10.1109/LSP.2018.2817176

Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning.

Nowadays doctors and radiologists are overwhelmed with a huge amount of work. This led to the effort to design different Computer-Aided Diagnosis systems (CAD system), with the aim of accomplishing a faster and more accurate diagnosis. The current development of deep learning is a big opportunity for the development of new CADs. In this paper, we propose a novel architecture for a convolutional neural network (CNN) ensemble for classifying chest X-ray (CRX) images into four classes: viral Pneumonia, Tuberculosis, COVID-19, and Healthy. Although Computed tomography (CT) is the best way to detect and diagnoses pulmonary issues, CT is more expensive than CRX. Furthermore, CRX is commonly the first step in the diagnosis, so it's very important to be accurate in the early stages of diagnosis and treatment. We applied the transfer learning technique and data augmentation to all CNNs for obtaining better performance. We have designed and evaluated two different CNN-ensembles: Stacking and Voting. This system is ready to be applied in a CAD system to automated diagnosis such a second or previous opinion before the doctors or radiology's. Our results show a great improvement, 99% accuracy of the Stacking Ensemble and 98% of accuracy for the the Voting Ensemble. To minimize missclassifications, we included six different base CNN models in our architecture (VGG16, VGG19, InceptionV3, ResNet101V2, DenseNet121 and CheXnet) and it could be extended to any number as well as we expect extend the number of diseases to detected. The proposed method has been validated using a large dataset created by mixing several public datasets with different image sizes and quality. As we demonstrate in the evaluation carried out, we reach better results and generalization compared with previous works. In addition, we make a first approach to explainable deep learning with the objective of providing professionals more information that may be valuable when evaluating CRXs.

BMC medical imaging

10.1186/s12880-022-00904-4 10.1007/s10489-020-01888-w 10.1016/j.eswa.2020.114054 10.1007/s10489-020-01902-1 10.3389/fmed.2020.00427 10.1016/j.imu.2020.100405 10.1007/s10462-020-09825-6 10.1016/j.media.2021.102121 10.3233/XST-200831 10.1055/s-0039-1677911 10.1186/s43055-021-00524-y 10.1016/j.cmpb.2020.105608 10.1016/j.eswa.2021.115141 10.1109/ACCESS.2020.3031384 10.1016/j.eswa.2021.115401 10.1109/TII.2021.3057683 10.1007/s13246-020-00966-0 10.1016/j.compeleceng.2019.08.004 10.1016/j.eswa.2020.113909 10.1007/s11263-015-0816-y 10.1148/rg.2017160032 10.3390/s21051742 10.1007/s10044-021-00970-4 10.1109/ACCESS.2020.2971257

Deep learning of longitudinal chest X-ray and clinical variables predicts duration on ventilator and mortality in COVID-19 patients.

To use deep learning of serial portable chest X-ray (pCXR) and clinical variables to predict mortality and duration on invasive mechanical ventilation (IMV) for Coronavirus disease 2019 (COVID-19) patients. This is a retrospective study. Serial pCXR and serial clinical variables were analyzed for data from day 1, day 5, day 1-3, day 3-5, or day 1-5 on IMV (110 IMV survivors and 76 IMV non-survivors). The outcome variables were duration on IMV and mortality. With fivefold cross-validation, the performance of the proposed deep learning system was evaluated by receiver operating characteristic (ROC) analysis and correlation analysis. Predictive models using 5-consecutive-day data outperformed those using 3-consecutive-day and 1-day data. Prediction using data closer to the outcome was generally better (i.e., day 5 data performed better than day 1 data, and day 3-5 data performed better than day 1-3 data). Prediction performance was generally better for the combined pCXR and non-imaging clinical data than either alone. The combined pCXR and non-imaging data of 5 consecutive days predicted mortality with an accuracy of 85 ± 3.5% (95% confidence interval (CI)) and an area under the curve (AUC) of 0.87 ± 0.05 (95% CI) and predicted the duration needed to be on IMV to within 2.56 ± 0.21 (95% CI) days on the validation dataset. Deep learning of longitudinal pCXR and clinical data have the potential to accurately predict mortality and duration on IMV in COVID-19 patients. Longitudinal pCXR could have prognostic value if these findings can be validated in a large, multi-institutional cohort.

Biomedical engineering online

10.1186/s12938-022-01045-z 10.1056/NEJMoa2001017 10.1016/S0140-6736(20)30183-5 10.1148/radiol.2020200642 10.1148/radiol.2020200463 10.1148/radiol.2020200432 10.1148/radiol.2020200370 10.1016/j.clinimag.2020.04.001 10.1007/s10140-020-01808-y 10.1097/RTI.0000000000000533 10.1148/radiol.2020201160 10.1001/jama.2020.6775 10.1177/08850666211033836 10.1056/NEJMp2006141 10.1161/CIRCULATIONAHA.115.001593 10.1590/0100-3984.2019.0049 10.1016/S1470-2045(19)30333-X 10.1371/journal.pone.0213653 10.1371/journal.pone.0221339 10.1148/radiol.2020201754 10.1007/s11547-020-01232-9 10.1371/journal.pone.0236621 10.7717/peerj.10309 10.1186/s12938-020-00831-x 10.1371/journal.pone.0236618 10.1093/infdis/jiaa447 10.1161/CIRCRESAHA.120.317134 10.32604/cmc.2020.010691 10.1093/cid/ciaa414 10.1056/NEJMoa2001316 10.1002/emp2.12205 10.3389/fmed.2021.661940 10.7150/ijms.51235 10.7717/peerj.10337 10.7717/peerj.11205 10.1109/TPAMI.2016.2572683

A novel abnormality annotation database for COVID-19 affected frontal lung X-rays.

Consistent clinical observations of characteristic findings of COVID-19 pneumonia on chest X-rays have attracted the research community to strive to provide a fast and reliable method for screening suspected patients. Several machine learning algorithms have been proposed to find the abnormalities in the lungs using chest X-rays specific to COVID-19 pneumonia and distinguish them from other etiologies of pneumonia. However, despite the enormous magnitude of the pandemic, there are very few instances of public databases of COVID-19 pneumonia, and to the best of our knowledge, there is no database with annotation of abnormalities on the chest X-rays of COVID-19 affected patients. Annotated databases of X-rays can be of significant value in the design and development of algorithms for disease prediction. Further, explainability analysis for the performance of existing or new deep learning algorithms will be enhanced significantly with access to ground-truth abnormality annotations. The proposed COVID Abnormality Annotation for X-Rays (CAAXR) database is built upon the BIMCV-COVID19+ database which is a large-scale dataset containing COVID-19+ chest X-rays. The primary contribution of this study is the annotation of the abnormalities in over 1700 frontal chest X-rays. Further, we define protocols for semantic segmentation as well as classification for robust evaluation of algorithms. We provide benchmark results on the defined protocols using popular deep learning models such as DenseNet, ResNet, MobileNet, and VGG for classification, and UNet, SegNet, and Mask-RCNN for semantic segmentation. The classwise accuracy, sensitivity, and AUC-ROC scores are reported for the classification models, and the IoU and DICE scores are reported for the segmentation models.

PloS one

10.1371/journal.pone.0271931 10.1016/j.tmaid.2020.101623 10.2214/AJR.20.22969 10.1007/s13246-020-00865-4 10.3390/sym12040651 10.1016/j.compbiomed.2020.103869 10.1109/TMI.2020.2993291 10.1016/j.compbiomed.2020.103792 10.1097/RTI.0000000000000533 10.1016/j.mehy.2020.109761 10.1038/s41598-020-76550-z 10.1371/journal.pone.0247176 10.1109/JBHI.2021.3111415 10.1007/s13278-021-00731-5 10.1016/j.media.2021.102046 10.1007/s10489-020-01900-3 10.1109/ACCESS.2020.3010287 10.1016/j.compbiomed.2021.104319 10.1109/TPAMI.2016.2644615 10.1371/journal.pmed.1002683 10.1126/science.aax2342 10.1109/TCYB.2019.2905157

An Intelligent Sensor Based Decision Support System for Diagnosing Pulmonary Ailment through Standardized Chest X-ray Scans.

Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time.

Sensors (Basel, Switzerland)

10.3390/s22197474 10.1152/physiolgenomics.00029.2020 10.1038/s41586-020-2008-3 10.1126/science.aba9757 10.1016/j.compbiomed.2020.103670 10.1016/j.ijid.2020.01.050 10.3390/e24040533 10.1016/j.cmpb.2020.105532 10.1016/j.compbiomed.2020.103792 10.1016/j.diii.2020.11.008 10.1148/radiol.2020203465 10.1016/j.diii.2021.05.006 10.1007/s10044-021-00958-0 10.1016/j.cmpb.2018.01.017 10.1016/j.ijmedinf.2018.06.003 10.1155/2021/6657533 10.1016/j.rmcr.2020.101265 10.1378/chest.125.6.2345 10.1007/s10140-020-01806-0 10.1148/radiol.2020202439 10.3390/s22166312 10.1016/j.ijmedinf.2022.104791 10.3390/jpm11100993 10.1109/TMI.2020.2993291 10.1109/TMI.2020.2996645 10.1038/s41598-020-76550-z 10.1007/s13246-020-00865-4 10.1007/s10489-020-01826-w 10.1016/j.cmpb.2020.105581 10.1007/s10044-021-00984-y 10.1007/s10489-020-01770-9 10.3390/s22155738 10.1016/j.cmpb.2020.105584 10.1002/jmv.25891 10.1148/radiol.2020202352 10.1183/13993003.02697-2020 10.1038/s41598-020-74164-z 10.1016/j.media.2021.102216 10.1016/j.cell.2018.02.010 10.1016/j.ecoinf.2016.11.006 10.1016/j.jocs.2017.10.006

Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics.

To develop a two-step machine learning (ML) based model to diagnose and predict involvement of lungs in COVID-19 and non COVID-19 pneumonia patients using CT chest radiomic features. Three hundred CT scans (3-classes: 100 COVID-19, 100 pneumonia, and 100 healthy subjects) were enrolled in this study. Diagnostic task included 3-class classification. Severity prediction score for COVID-19 and pneumonia was considered as mild (0-25%), moderate (26-50%), and severe (>50%). Whole lungs were segmented utilizing deep learning-based segmentation. Altogether, 107 features including shape, first-order histogram, second and high order texture features were extracted. Pearson correlation coefficient (PCC≥90%) followed by different features selection algorithms were employed. ML-based supervised algorithms (Naïve Bays, Support Vector Machine, Bagging, Random Forest, K-nearest neighbors, Decision Tree and Ensemble Meta voting) were utilized. The optimal model was selected based on precision, recall and area-under-curve (AUC) by randomizing the training/validation, followed by testing using the test set. Nine pertinent features (2 shape, 1 first-order, and 6 second-order) were obtained after features selection for both phases. In diagnostic task, the performance of 3-class classification using Random Forest was 0.909±0.026, 0.907±0.056, 0.902±0.044, 0.939±0.031, and 0.982±0.010 for precision, recall, F1-score, accuracy, and AUC, respectively. The severity prediction task using Random Forest achieved 0.868±0.123 precision, 0.865±0.121 recall, 0.853±0.139 F1-score, 0.934±0.024 accuracy, and 0.969±0.022 AUC. The two-phase ML-based model accurately classified COVID-19 and pneumonia patients using CT radiomics, and adequately predicted severity of lungs involvement. This 2-steps model showed great potential in assessing COVID-19 CT images towards improved management of patients.

Computers in biology and medicine

10.1016/j.compbiomed.2022.106165 10.1016/j.pdpdt.2021.102287 10.1016/j.compbiomed.2021.104665 10.1148/radiol.2020201160 10.1148/radiol.2021202553 10.1016/S1120-1797(22)00087-4 10.1016/j.compbiomed.2021.104304 10.1038/s41598-021-88807-2 10.1007/s42979-020-00394-7 10.48550/arXiv.2003.11988 10.1186/s12967-020-02692-3 10.1007/s11432-020-2849-3 10.1016/j.acra.2020.09.004 10.1016/j.compbiomed.2022.105467 10.1038/s41598-022-18994-z 10.1002/ima.22672 10.1148/radiol.2020201473 10.1148/radiol.2020200370 10.1148/radiol.2462070712 10.1148/radiol.11092149 10.1148/ryct.2020200322 10.1148/radiol.2020200463 10.30476/ijms.2021.88036.1858.20 10.1148/radiol.2020191145 10.1186/s40644-020-00311-4 10.1002/mp.13649 10.1016/j.ejro.2020.100271 10.1101/2021.12.07.21267367 10.2967/jnumed.121.262567 10.1007/s00330-020-06829-2 10.1088/1361-6560/abe838 10.1109/ICSPIS51611.2020.9349605 10.21037/atm-20-3026 10.1155/2021/2263469 10.1016/j.media.2020.10182 10.1007/s12539-020-00410-7 10.1186/s12880-021-00564-w 10.1016/j.smhl.2020.100178 10.1016/j.csbj.2021.06.022 10.1038/s41598-021-96755-0 10.1016/j.bspc.2022.103662 10.21037/qims.2020.02.21 10.3389/fdgth.2021.662343 10.1186/s43055-021-00592-0 10.1007/s00259-020-05075-4 10.1038/s41598-021-99015-3 10.1016/j.ijid.2021.03.008 10.1016/j.compbiomed.2021.104531 10.1148/ryct.2020200047

Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening.

Coronavirus Disease 2019 (COVID-19) is currently a global pandemic, and early screening is one of the key factors for COVID-19 control and treatment. Here, we developed and validated chest CT-based imaging biomarkers for COVID-19 patient screening from two independent hospitals with 419 patients. We identified the vasculature-like signals from CT images and found that, compared to healthy and community acquired pneumonia (CAP) patients, COVID-19 patients display a significantly higher abundance of these signals. Furthermore, unsupervised feature learning led to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that have been double-blindly validated in an independent hospital (sensitivity: 0.941, specificity: 0.920, AUC: 0.971, accuracy 0.931, F1 score: 0.929). Our findings could open a new avenue to assist screening of COVID-19 patients.

Frontiers in public health

10.3389/fpubh.2022.1004117 10.1111/tmi.13383 10.1002/jmv.25722 10.1080/14737159.2020.1757437 10.1148/radiol.2020200343 10.1111/eci.13706 10.1371/journal.pone.0242958 10.1016/j.radi.2020.09.010 10.1016/j.ijid.2020.04.023 10.1007/s11604-020-00948-y 10.2214/AJR.20.22961 10.1148/radiol.2020200642 10.1148/radiol.2020200330 10.1148/radiol.2020200432 10.1177/0846537120913033 10.1371/journal.pone.0263916 10.1016/j.compbiomed.2022.105298 10.2196/27468 10.1155/2021/9868517 10.1200/CCI.19.00155 10.1016/j.patcog.2014.10.005 10.1109/83.902291 10.1007/s11263-014-0790-9 10.1016/j.acra.2020.03.003 10.1097/TP.0000000000003412 10.1056/NEJMoa2015432 10.1038/s41591-020-0822-7 10.1016/j.tjem.2018.08.001 10.1089/ars.2012.5149 10.1093/cvr/cvaa078 10.1016/j.jcv.2020.104362 10.1146/annurev-virology-031413-085548 10.1016/0891-5849(96)00131-1 10.1371/journal.ppat.1008536 10.1038/s41577-020-0311-8 10.1016/S0140-6736(20)30937-5 10.1016/j.bcp.2009.04.029 10.1148/radiol.2020200905 10.1016/j.cell.2020.04.045 10.1038/s41591-020-0931-3 10.1109/TMI.2020.2996256 10.21037/qims.2020.04.02 10.1016/S1473-3099(20)30241-3 10.1148/radiol.2020201845 10.1148/radiol.2020201365 10.1038/d41586-020-01001-8 10.1136/bmj.m1367

RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning.

To demonstrate the value of pretraining with millions of radiologic images compared with ImageNet photographic images on downstream medical applications when using transfer learning. This retrospective study included patients who underwent a radiologic study between 2005 and 2020 at an outpatient imaging facility. Key images and associated labels from the studies were retrospectively extracted from the original study interpretation. These images were used for RadImageNet model training with random weight initiation. The RadImageNet models were compared with ImageNet models using the area under the receiver operating characteristic curve (AUC) for eight classification tasks and using Dice scores for two segmentation problems. The RadImageNet database consists of 1.35 million annotated medical images in 131 872 patients who underwent CT, MRI, and US for musculoskeletal, neurologic, oncologic, gastrointestinal, endocrine, abdominal, and pulmonary pathologic conditions. For transfer learning tasks on small datasets-thyroid nodules (US), breast masses (US), anterior cruciate ligament injuries (MRI), and meniscal tears (MRI)-the RadImageNet models demonstrated a significant advantage ( RadImageNet pretrained models demonstrated better interpretability compared with ImageNet models, especially for smaller radiologic datasets.

Radiology. Artificial intelligence

10.1148/ryai.210315

Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement.

In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteristics in a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process. This technique forces the models to identify pulmonary features from the images and penalizes them for learning features that can discriminate between the original datasets that the images come from. We find that models trained in this way indeed have better generalization performance on unseen data; in the best case we found that it improved AUC by 0.13 on held out data. We further find that this outperforms masking out non-lung parts of the CXRs and performing histogram equalization, both of which are recently proposed methods for removing biases in CXR datasets.

PloS one

10.1371/journal.pone.0274098 10.1016/j.dsx.2020.04.012 10.1016/j.compbiomed.2020.103792 10.3389/fmed.2020.00427 10.1007/s13246-020-00865-4 10.1136/bmj.m1328 10.1016/j.cmpb.2020.105532 10.1101/2020.09.13.20193565 10.1016/j.cell.2020.04.045

Automatic Detection of Cases of COVID-19 Pneumonia from Chest X-ray Images and Deep Learning Approaches.

Machine learning has already been used as a resource for disease detection and health care as a complementary tool to help with various daily health challenges. The advancement of deep learning techniques and a large amount of data-enabled algorithms to outperform medical teams in certain imaging tasks, such as pneumonia detection, skin cancer classification, hemorrhage detection, and arrhythmia detection. Automated diagnostics, which are enabled by images extracted from patient examinations, allow for interesting experiments to be conducted. This research differs from the related studies that were investigated in the experiment. These works are capable of binary categorization into two categories. COVID-Net, for example, was able to identify a positive case of COVID-19 or a healthy person with 93.3% accuracy. Another example is CHeXNet, which has a 95% accuracy rate in detecting cases of pneumonia or a healthy state in a patient. Experiments revealed that the current study was more effective than the previous studies in detecting a greater number of categories and with a higher percentage of accuracy. The results obtained during the model's development were not only viable but also excellent, with an accuracy of nearly 96% when analyzing a chest X-ray with three possible diagnoses in the two experiments conducted.

Computational intelligence and neuroscience

10.1155/2022/7451551 10.1186/s41256-020-00135-6 10.1186/s13643-021-01648-y 10.21203/rs.3.rs-198847/v1 10.1080/22221751.2020.1772678 10.1016/j.media.2020.101794 10.1155/2021/8148772 10.21931/RB/2020.05.03.19 10.1109/access.2020.3010226 10.3233/XST-200715 10.2991/ijcis.d.210518.001 10.1016/j.cmpb.2020.105532 10.1155/2022/4569879 10.1504/ijcsm.2022.122146 10.1016/j.matpr.2021.05.553 10.1016/j.ijin.2020.12.002 10.1007/s00521-022-06918-x 10.3390/s22031211 10.1155/2021/5759184 10.32604/csse.2022.022014 10.1016/j.bbe.2020.08.008