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Biomarkers extracted by fully automated body composition analysis from chest CT correlate with SARS-CoV-2 outcome severity.

The complex process of manual biomarker extraction from body composition analysis (BCA) has far restricted the analysis of SARS-CoV-2 outcomes to small patient cohorts and a limited number of tissue types. We investigate the association of two BCA-based biomarkers with the development of severe SARS-CoV-2 infections for 918 patients (354 female, 564 male) regarding disease severity and mortality (186 deceased). Multiple tissues, such as muscle, bone, or adipose tissue are used and acquired with a deep-learning-based, fully-automated BCA from computed tomography images of the chest. The BCA features and markers were univariately analyzed with a Shapiro-Wilk and two-sided Mann-Whitney-U test. In a multivariate approach, obtained markers were adjusted by a defined set of laboratory parameters promoted by other studies. Subsequently, the relationship between the markers and two endpoints, namely severity and mortality, was investigated with regard to statistical significance. The univariate approach showed that the muscle volume was significant for female (p

Scientific reports

10.1038/s41598-022-20419-w 10.1016/j.dsx.2020.06.060 10.2196/26075 10.1038/s41586-020-2521-4 10.1016/S2213-8587(21)00089-9 10.1016/j.clnesp.2020.09.018 10.1186/s12911-021-01576-w 10.1016/j.imu.2021.100564 10.1016/j.isci.2021.103523 10.1016/j.metabol.2020.154378 10.1093/bja/aev541 10.1007/s00261-020-02693-2 10.1186/s12933-021-01327-1 10.1002/oby.22971 10.1080/17512433.2017.1347503 10.1016/j.ejca.2015.12.030 10.1002/jcsm.12379 10.1097/RTI.0000000000000428 10.1002/jcsm.12573 10.1007/s00330-020-07147-3 10.1016/j.ejrad.2021.110031 10.1038/s41598-021-00161-5 10.1111/nyas.12842 10.3390/jcm10020356 10.1038/oby.2008.575 10.1038/ncpcardio0319 10.1016/j.numecd.2013.11.010 10.3389/fphys.2021.651167 10.1007/s11906-019-0939-6 10.1093/ehjci/jeu006 10.1016/j.jcct.2017.11.007 10.1016/j.amjcard.2016.01.033 10.5812/ijem.3505 10.1038/s41592-019-0686-2 10.1016/j.metabol.2020.154436 10.1148/radiol.2021204141

AI in Health Science: A Perspective.

By helping practitioners understand complicated and varied types of data, Artificial Intelligence (AI) has influenced medical practice deeply. It is the use of a computer to mimic intelligent behaviour. Many medical professions, particularly those reliant on imaging or surgery, are progressively developing AI. While AI cognitive component outperforms human intellect, it lacks awareness, emotions, intuition, and adaptability. With minimum human participation, AI is quickly growing in healthcare, and numerous AI applications have been created to address current issues. This article explains AI, its various elements and how to utilize them in healthcare. It also offers practical suggestions for developing an AI strategy to assist the digital healthcare transition.

Current pharmaceutical biotechnology

10.2174/1389201023666220929145220

COVID-19 Semantic Pneumonia Segmentation and Classification Using Artificial Intelligence.

Coronavirus 2019 (COVID-19) has become a pandemic. The seriousness of COVID-19 can be realized from the number of victims worldwide and large number of deaths. This paper presents an efficient deep semantic segmentation network (DeepLabv3Plus). Initially, the dynamic adaptive histogram equalization is utilized to enhance the images. Data augmentation techniques are then used to augment the enhanced images. The second stage builds a custom convolutional neural network model using several pretrained ImageNet models and compares them to repeatedly trim the best-performing models to reduce complexity and improve memory efficiency. Several experiments were done using different techniques and parameters. Furthermore, the proposed model achieved an average accuracy of 99.6% and an area under the curve of 0.996 in the COVID-19 detection. This paper will discuss how to train a customized smart convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.

Contrast media & molecular imaging

10.1155/2022/5297709 10.1109/tnnls.2020.2995800 10.1109/tnnls.2017.2766168 10.1016/j.media.2020.101836 10.1109/access.2020.3016780 10.1016/j.mehy.2020.109761 10.1016/j.imu.2020.100360 10.1016/j.knosys.2020.106270 10.1016/j.asoc.2020.106580 10.1109/ICNN.1993.298572 10.1109/tmi.2016.2528162 10.1007/s13246-020-00888-x 10.1016/j.compbiomed.2020.103792 10.1001/jama.2020.3786 10.1007/978-981-16-7618-5_3 10.1016/j.eswa.2020.114054 10.7717/peerj-cs.358 10.1109/tgrs.2022.3155765 10.1504/ijhm.2021.114174 10.1109/access.2020.2971257 10.1038/s41598-020-76550-z 10.1101/2020.03.26.20044610 10.1016/j.cmpb.2020.105532 10.3389/fpubh.2020.00437 10.1109/access.2020.3003810 10.1016/j.patrec.2020.04.016 10.1109/access.2019.2941937 10.1504/ijhm.2021.114173 10.1109/access.2021.3120717 10.1504/ijhm.2021.120616 10.1109/access.2021.3101142

A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques.

COVID-19 outbreak has become one of the most challenging problems for human being. It is a communicable disease caused by a new coronavirus strain, which infected over 375 million people already and caused almost 6 million deaths. This paper aims to develop and design a framework for early diagnosis and fast classification of COVID-19 symptoms using multimodal Deep Learning techniques. we collected chest X-ray and cough sample data from open source datasets, Cohen and datasets and local hospitals. The features are extracted from the chest X-ray images are extracted from chest X-ray datasets. We also used cough audio datasets from Coswara project and local hospitals. The publicly available Coughvid DetectNow and Virufy datasets are used to evaluate COVID-19 detection based on speech sounds, respiratory, and cough. The collected audio data comprises slow and fast breathing, shallow and deep coughing, spoken digits, and phonation of sustained vowels. Gender, geographical location, age, preexisting medical conditions, and current health status (COVID-19 and Non-COVID-19) are recorded. The proposed framework uses the selection algorithm of the pre-trained network to determine the best fusion model characterized by the pre-trained chest X-ray and cough models. Third, deep chest X-ray fusion by discriminant correlation analysis is used to fuse discriminatory features from the two models. The proposed framework achieved recognition accuracy, specificity, and sensitivity of 98.91%, 96.25%, and 97.69%, respectively. With the fusion method we obtained 94.99% accuracy. This paper examines the effectiveness of well-known ML architectures on a joint collection of chest-X-rays and cough samples for early classification of COVID-19. It shows that existing methods can effectively used for diagnosis and suggesting that the fusion learning paradigm could be a crucial asset in diagnosing future unknown illnesses. The proposed framework supports health informatics basis on early diagnosis, clinical decision support, and accurate prediction.

Computer methods and programs in biomedicine

10.1016/j.cmpb.2022.107109 10.1109/TDSC.2022.3144657

A Deep Learning based Solution (Covi-DeteCT) Amidst COVID-19.

The whole world has been severely affected due to the COVID-19 pandemic. The rapid and large-scale spread has caused immense pressure on the medical sector hence increasing the chances of false detection due to human errors and mishandling of reports. At the time of outbreaks of COVID-19, there is a crucial shortage of test kits as well. Quick diagnostic testing has become one of the main challenges. For the detection of COVID-19, many Artificial Intelligence based methodologies have been proposed, a few had suggested integration of the model on a public usable platform, but none had executed this on a working application as per our knowledge. Keeping the above comprehension in mind, the objective is to provide an easy-to-use platform for COVID-19 identification. This work would be a contribution to the digitization of health facilities. This work is a fusion of deep learning classifiers and medical images to provide a speedy and accurate identification of the COVID-19 virus by analyzing the user's CT scan images of the lungs. It will assist healthcare workers in reducing their workload and decreasing the possibility of false detection. In this work, various models like Resnet50V2 and Resnet101V2, an adjusted rendition of ResNet101V2 with Feature Pyramid Network, have been applied for classifying the CT scan images into the categories: normal or COVID-19 positive. A detailed analysis of all three models' performances have been done on the SARS-CoV-2 dataset with various metrics like precision, recall, F1-score, ROC curve, etc. It was found that Resnet50V2 achieves an accuracy of 96.79%, whereas Resnet101V2 achieves an accuracy of 97.79%. An accuracy of 98.19% has been obtained by ResNet101V2 with Feature Pyramid Network. As Res- Net101V2 with Feature Pyramid Network is showing better results, thus, it is further incorporated into a working application that takes CT images as input from the user and feeds into the trained model and detects the presence of COVID-19 infection. A mobile application integrated with the deeper variant of ResNet, i.e., ResNet101V2 with FPN checks the presence of COVID-19 in a faster and accurate manner. People can use this application on their smart mobile devices. This automated system would assist healthcare workers as well, which ultimately reduces their workload and decreases the possibility of false detection.

Current medical imaging

10.2174/1573405618666220928145344

IEViT: An enhanced vision transformer architecture for chest X-ray image classification.

Chest X-ray imaging is a relatively cheap and accessible diagnostic tool that can assist in the diagnosis of various conditions, including pneumonia, tuberculosis, COVID-19, and others. However, the requirement for expert radiologists to view and interpret chest X-ray images can be a bottleneck, especially in remote and deprived areas. Recent advances in machine learning have made possible the automated diagnosis of chest X-ray scans. In this work, we examine the use of a novel Transformer-based deep learning model for the task of chest X-ray image classification. We first examine the performance of the Vision Transformer (ViT) state-of-the-art image classification machine learning model for the task of chest X-ray image classification, and then propose and evaluate the Input Enhanced Vision Transformer (IEViT), a novel enhanced Vision Transformer model that can achieve improved performance on chest X-ray images associated with various pathologies. Experiments on four chest X-ray image data sets containing various pathologies (tuberculosis, pneumonia, COVID-19) demonstrated that the proposed IEViT model outperformed ViT for all the data sets and variants examined, achieving an F1-score between 96.39% and 100%, and an improvement over ViT of up to +5.82% in terms of F1-score across the four examined data sets. IEViT's maximum sensitivity (recall) ranged between 93.50% and 100% across the four data sets, with an improvement over ViT of up to +3%, whereas IEViT's maximum precision ranged between 97.96% and 100% across the four data sets, with an improvement over ViT of up to +6.41%. Results showed that the proposed IEViT model outperformed all ViT's variants for all the examined chest X-ray image data sets, demonstrating its superiority and generalisation ability. Given the relatively low cost and the widespread accessibility of chest X-ray imaging, the use of the proposed IEViT model can potentially offer a powerful, but relatively cheap and accessible method for assisting diagnosis using chest X-ray images.

Computer methods and programs in biomedicine

10.1016/j.cmpb.2022.107141

E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network.

Recently,  the coronavirus disease 2019 (COVID-19)  has caused mortality of many people globally. Thus, there existed a need to detect this disease to prevent its further spread. Hence, the study aims to predict COVID-19 infected patients based on deep learning (DL) and image processing. The study intends to classify the normal and abnormal cases of COVID-19 by considering three different medical imaging modalities namely ultrasound imaging, X-ray images and CT scan images through introduced attention bottleneck residual network (AB-ResNet). It also aims to segment the abnormal infected area from normal images for localizing localising the disease infected area through the proposed edge based graph cut segmentation (E-GCS). AB-ResNet is used for classifying images whereas E-GCS segment the abnormal images. The study possess various advantages as it rely on DL and possess capability for accelerating the training speed of deep networks. It also enhance the network depth leading to minimum parameters, minimising the impact of vanishing gradient issue and attaining effective network performance with respect to better accuracy. Performance and comparative analysis is undertaken to evaluate the efficiency of the introduced system and results explores the efficiency of the proposed system in COVID-19 detection with high accuracy (99%).

Engineering applications of artificial intelligence

10.1016/j.engappai.2022.105398

Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review.

Covid-19 is now one of the most incredibly intense and severe illnesses of the twentieth century. Covid-19 has already endangered the lives of millions of people worldwide due to its acute pulmonary effects. Image-based diagnostic techniques like X-ray, CT, and ultrasound are commonly employed to get a quick and reliable clinical condition. Covid-19 identification out of such clinical scans is exceedingly time-consuming, labor-intensive, and susceptible to silly intervention. As a result, radiography imaging approaches using Deep Learning (DL) are consistently employed to achieve great results. Various artificial intelligence-based systems have been developed for the early prediction of coronavirus using radiography pictures. Specific DL methods such as CNN and RNN noticeably extract extremely critical characteristics, primarily in diagnostic imaging. Recent coronavirus studies have used these techniques to utilize radiography image scans significantly. The disease, as well as the present pandemic, was studied using public and private data. A total of 64 pre-trained and custom DL models concerning imaging modality as taxonomies are selected from the studied articles. The constraints relevant to DL-based techniques are the sample selection, network architecture, training with minimal annotated database, and security issues. This includes evaluating causal agents, pathophysiology, immunological reactions, and epidemiological illness. DL-based Covid-19 detection systems are the key focus of this review article. Covid-19 work is intended to be accelerated as a result of this study.

Neural processing letters

10.1007/s11063-022-11023-0 10.1101/2020.02.25.20021568 10.1016/j.scs.2020.102571 10.1109/ACCESS.2021.3058066 10.1002/jmv.26709 10.1109/ACCESS.2020.3003810 10.33889/IJMEMS.2020.5.4.052 10.1080/14737159.2021.1962708 10.1016/j.matpr.2020.06.245 10.1016/j.jiph.2020.03.019 10.1080/14737159.2020.1757437 10.1109/ACCESS.2021.3054484 10.1080/14737159.2020.1816466 10.1109/JBHI.2020.3030224 10.1007/s12559-020-09787-5 10.1080/07391102.2020.1767212 10.1016/j.scs.2021.102777 10.1007/s10489-020-01831-z 10.1109/RBME.2020.2990959 10.1007/s00521-020-05437-x 10.1097/RLI.0000000000000748 10.1148/radiol.2020200905 10.1109/ACCESS.2021.3058537 10.1016/j.csbj.2021.02.016 10.1016/j.chaos.2020.110120 10.2196/19569 10.1007/s00500-020-05424-3 10.1038/s41746-021-00399-3 10.1016/j.ijleo.2021.166405 10.1109/TII.2021.3057683 10.1016/j.chaos.2020.110245 10.1109/JBHI.2020.3037127 10.1016/j.asoc.2021.107184 10.1016/j.irbm.2021.01.004 10.1016/j.ejrad.2020.109041 10.1016/j.aej.2021.01.011 10.1016/j.asoc.2020.106859 10.1016/j.chaos.2020.110190 10.1016/j.asoc.2020.106744 10.1016/j.asoc.2020.106885 10.1007/s10489-020-01902-1 10.1007/s13246-020-00865-4 10.1038/s41598-020-76550-z 10.7937/tcia.2020.6c7y-gq39 10.7910/DVN/6ACUZJ 10.1016/j.ejrad.2020.109402 10.21037/atm.2020.03.132 10.1016/j.chaos.2020.110495 10.1016/j.imu.2020.100505 10.1016/j.eswa.2020.114054 10.1016/j.asoc.2021.107160 10.1109/ACCESS.2020.3016780 10.1016/j.mehy.2020.109761 10.1016/j.imu.2020.100427 10.1007/s10140-020-01886-y 10.1016/j.media.2020.101913 10.1016/j.iot.2021.100377 10.1002/ima.22706,32,2,(419-434) 10.1007/s00264-020-04609-7 10.1016/j.bspc.2021.102490 10.1016/j.knosys.2020.106647 10.1016/j.ibmed.2020.100013 10.1016/j.imu.2020.100506 10.1016/j.scs.2020.102589 10.3390/app11020672 10.1016/j.advms.2020.06.005 10.1109/TMI.2020.2994459 10.1016/j.media.2021.101993 10.1016/j.compeleceng.2020.106960 10.1109/ACCESS.2020.3005510 10.1109/ACCESS.2020.2994762 10.3348/kjr.2020.0536 10.3390/electronics9091439 10.1007/s42600-021-00132-9 10.1186/s40537-020-00392-9 10.1016/j.compbiomed.2020.103792

Analysis of the Causes of Solitary Pulmonary Nodule Misdiagnosed as Lung Cancer by Using Artificial Intelligence: A Retrospective Study at a Single Center.

Artificial intelligence (AI) adopting deep learning technology has been widely used in the med-ical imaging domain in recent years. It realized the automatic judgment of benign and malig-nant solitary pulmonary nodules (SPNs) and even replaced the work of doctors to some extent. However, misdiagnoses can occur in certain cases. Only by determining the causes can AI play a larger role. A total of 21 Coronavirus disease 2019 (COVID-19) patients were diagnosed with SPN by CT imaging. Their Clinical data, including general condition, imaging features, AI re-ports, and outcomes were included in this retrospective study. Although they were confirmed COVID-19 by testing reverse transcription-polymerase chain reaction (RT-PCR) with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), their CT imaging data were misjudged by AI to be high-risk nodules for lung cancer. Imaging characteristics included burr sign (76.2%), lobulated sign (61.9%), pleural indentation (42.9%), smooth edges (23.8%), and cavity (14.3%). The accuracy of AI was different from that of radiologists in judging the nature of be-nign SPNs (p < 0.001, κ = 0.036 < 0.4, means the two diagnosis methods poor fit). COVID-19 patients with SPN might have been misdiagnosed using the AI system, suggesting that the AI system needs to be further optimized, especially in the event of a new disease outbreak.

Diagnostics (Basel, Switzerland)

10.3390/diagnostics12092218 10.1016/j.chest.2017.01.018 10.1148/radiol.2017161659 10.1109/TMI.2016.2629462 10.1016/j.media.2017.06.015 10.1002/mp.12846 10.1109/TBME.2016.2613502 10.1038/srep24454 10.1016/j.cell.2020.04.045 10.3390/cancers12082211 10.1056/NEJMoa2001316 10.1155/2017/4067832 10.1038/srep46479 10.1371/journal.pone.0248957 10.1016/j.jtho.2020.04.030 10.1016/j.jinf.2020.03.033 10.1007/s00330-020-07042-x 10.1097/CM9.0000000000000634 10.3390/cancers11111673 10.1111/1759-7714.13185 10.1016/j.cell.2018.02.010 10.1136/thoraxjnl-2019-214104 10.1038/s41586-020-2012-7 10.1016/j.compbiomed.2012.12.004 10.1038/nbt.4233 10.1136/bmj.m606

Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans.

Background and Motivation: COVID-19 has resulted in a massive loss of life during the last two years. The current imaging-based diagnostic methods for COVID-19 detection in multiclass pneumonia-type chest X-rays are not so successful in clinical practice due to high error rates. Our hypothesis states that if we can have a segmentation-based classification error rate <5%, typically adopted for 510 (K) regulatory purposes, the diagnostic system can be adapted in clinical settings. Method: This study proposes 16 types of segmentation-based classification deep learning-based systems for automatic, rapid, and precise detection of COVID-19. The two deep learning-based segmentation networks, namely UNet and UNet+, along with eight classification models, namely VGG16, VGG19, Xception, InceptionV3, Densenet201, NASNetMobile, Resnet50, and MobileNet, were applied to select the best-suited combination of networks. Using the cross-entropy loss function, the system performance was evaluated by Dice, Jaccard, area-under-the-curve (AUC), and receiver operating characteristics (ROC) and validated using Grad-CAM in explainable AI framework. Results: The best performing segmentation model was UNet, which exhibited the accuracy, loss, Dice, Jaccard, and AUC of 96.35%, 0.15%, 94.88%, 90.38%, and 0.99 (p-value <0.0001), respectively. The best performing segmentation-based classification model was UNet+Xception, which exhibited the accuracy, precision, recall, F1-score, and AUC of 97.45%, 97.46%, 97.45%, 97.43%, and 0.998 (p-value <0.0001), respectively. Our system outperformed existing methods for segmentation-based classification models. The mean improvement of the UNet+Xception system over all the remaining studies was 8.27%. Conclusion: The segmentation-based classification is a viable option as the hypothesis (error rate <5%) holds true and is thus adaptable in clinical practice.

Diagnostics (Basel, Switzerland)

10.3390/diagnostics12092132 10.1371/journal.pone.0249788 10.1016/j.compbiomed.2020.103960 10.1186/s13244-022-01176-w 10.1007/s10554-020-02089-9 10.1001/jama.2020.3786 10.1016/j.acra.2015.12.010 10.1148/radiol.2015150425 10.1118/1.2836950 10.1016/j.ejrad.2019.02.038 10.1038/s42256-020-0186-1 10.1016/j.zemedi.2018.11.002 10.1016/j.compbiomed.2018.05.014 10.3390/cancers11010111 10.1007/s10916-021-01707-w 10.1007/s11548-021-02317-0 10.1016/j.cmpb.2020.105581 10.1016/j.chaos.2020.110495 10.1007/s10489-020-01902-1 10.1016/j.bspc.2020.102365 10.1148/radiol.2020203511 10.3390/diagnostics12061482 10.1016/j.cmpb.2017.07.011 10.3390/biomedicines9070720 10.1016/j.compbiomed.2016.06.010 10.1016/j.compbiomed.2020.103847 10.1016/j.compbiomed.2021.104721 10.23736/S0392-9590.21.04771-4 10.3390/diagnostics12051283 10.3390/diagnostics11112109 10.1016/j.compbiomed.2020.103958 10.1109/ACCESS.2021.3086020 10.2352/J.ImagingSci.Technol.2020.64.2.020508 10.1109/TIM.2022.3174270 10.1007/s11222-009-9153-8 10.1109/TKDE.2019.2912815 10.1016/j.neucom.2018.05.011 10.1109/JBHI.2022.3177854 10.1109/ACCESS.2020.3031384 10.1109/ACCESS.2020.3010287 10.1016/j.compbiomed.2021.104319 10.17632/rscbjbr9sj.2 10.1016/j.cell.2018.02.010 10.1016/j.compbiomed.2022.105571 10.1016/j.compbiomed.2017.10.022 10.1109/ACCESS.2019.2962617 10.1007/s13755-021-00166-4 10.3390/diagnostics12030652 10.1016/j.eswa.2021.116288 10.1007/s11277-018-5777-3 10.1007/s11277-018-5702-9 10.1186/s12880-020-00514-y 10.1109/ACCESS.2020.3017915 10.3390/s21217116 10.1016/j.cmpb.2019.06.005 10.1038/s41598-021-99015-3 10.1016/j.asoc.2020.106912 10.3390/s22031211 10.1109/TMI.2020.2993291 10.1007/s00330-021-08050-1 10.1016/j.bspc.2021.103182 10.1016/j.bea.2022.100041 10.1016/j.compbiomed.2022.105244 10.1016/j.neucom.2021.03.034 10.1007/s10916-016-0504-7 10.1016/j.ejrad.2022.110164 10.1142/S0219467801000402 10.1016/j.mri.2012.04.021 10.1007/s10554-020-02124-9 10.3390/sym14071310

Identification of micro- and nanoplastics released from medical masks using hyperspectral imaging and deep learning.

Apart from other severe consequences, the COVID-19 pandemic has inflicted a surge in personal protective equipment usage, some of which, such as medical masks, have a short effective protection time. Their misdisposition and subsequent natural degradation make them huge sources of micro- and nanoplastic particles. To better understand the consequences of the direct influence of microplastic pollution on biota, there is an urgent need to develop a reliable and high-throughput analytical tool for sub-micrometre plastic identification and visualisation in environmental and biological samples. This study evaluated the application of a combined technique based on dark-field enhanced microscopy and hyperspectral imaging augmented with deep learning data analysis for the visualisation, detection and identification of microplastic particles released from commercially available medical masks after 192 hours of UV-C irradiation. The analysis was performed using a separated blue-coloured spunbond outer layer and white-coloured meltblown interlayer that allowed us to assess the influence of the structure and pigmentation of intact and UV-exposed samples on classification performance. Microscopy revealed strong fragmentation of both layers and the formation of microparticles and fibres of various shapes after UV exposure. Based on the spectral signatures of both layers, it was possible to identify intact materials using a convolutional neural network successfully. However, the further classification of UV-exposed samples demonstrated that the spectral characteristics of samples in the visible to near-infrared range are disrupted, causing a decreased performance of the CNN. Despite this, the application of a deep learning algorithm in hyperspectral analysis outperformed the conventional spectral angle mapper technique in classifying both intact and UV-exposed samples, confirming the potential of the proposed approach in secondary microplastic analysis.

The Analyst

10.1039/d2an01139e

Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients.

Automated image segmentation is an essential step in quantitative image analysis. This study assesses the performance of a deep learning-based model for lung segmentation from computed tomography (CT) images of normal and COVID-19 patients. A descriptive-analytical study was conducted from December 2020 to April 2021 on the CT images of patients from various educational hospitals affiliated with Mashhad University of Medical Sciences (Mashhad, Iran). Of the selected images and corresponding lung masks of 1,200 confirmed COVID-19 patients, 1,080 were used to train a residual neural network. The performance of the residual network (ResNet) model was evaluated on two distinct external test datasets, namely the remaining 120 COVID-19 and 120 normal patients. Different evaluation metrics such as Dice similarity coefficient (DSC), mean absolute error (MAE), relative mean Hounsfield unit (HU) difference, and relative volume difference were calculated to assess the accuracy of the predicted lung masks. The Mann-Whitney U test was used to assess the difference between the corresponding values in the normal and COVID-19 patients. P<0.05 was considered statistically significant. The ResNet model achieved a DSC of 0.980 and 0.971 and a relative mean HU difference of -2.679% and -4.403% for the normal and COVID-19 patients, respectively. Comparable performance in lung segmentation of normal and COVID-19 patients indicated the model's accuracy for identifying lung tissue in the presence of COVID-19-associated infections. Although a slightly better performance was observed in normal patients. The ResNet model provides an accurate and reliable automated lung segmentation of COVID-19 infected lung tissue.A preprint version of this article was published on arXiv before formal peer review (https://arxiv.org/abs/2104.02042).

Iranian journal of medical sciences

10.30476/IJMS.2022.90791.2178 10.30476/ijms.2020.85869.1549 10.30476/ijms.2020.87233.1730 10.1109/TMI.2020.3001810 10.1109/TBDATA.2021.3056564 10.1109/TMI.2020.2996645 10.1016/j.media.2020.101794 10.1109/ICBME.2010.5704968 10.1016/j.radphyschem.2021.109666 10.1148/radiol.2020200642 10.1101/2020.03.12.20027185 10.1016/j.cell.2020.04.045 10.1148/radiol.2020202439 10.1186/s41747-020-00173-2 10.1016/j.media.2016.11.003 10.1038/s41598-020-80936-4 10.1016/j.procs.2018.01.104 10.1186/s41824-020-00086-8 10.1002/ima.22527 10.1002/mp.14418 10.1016/j.media.2020.101759 10.1016/j.media.2016.02.002 10.1109/TMI.2021.3066161 10.1109/TMI.2020.2995965 10.1016/j.patcog.2021.108109 10.1016/j.patcog.2020.107747 10.1515/cdbme-2016-0114 10.1016/j.cmpb.2018.01.025 10.1016/j.media.2020.101718 10.1016/j.chest.2020.04.003 10.1148/radiol.2020200432 10.1002/mp.14676 10.1109/RBME.2020.2987975 10.3390/sym12040651 10.3892/etm.2020.9210 10.1016/j.imu.2021.100681 10.3390/diagnostics11081405 10.1016/j.patcog.2021.108071 10.1136/neurintsurg-2015-011697

Detection of COVID-19 Infection in CT and X-ray images using transfer learning approach.

The infection caused by the SARS-CoV-2 (COVID-19) pandemic is a threat to human lives. An early and accurate diagnosis is necessary for treatment. The study presents an efficient classification methodology for precise identification of infection caused by COVID-19 using CT and X-ray images. The depthwise separable convolution-based model of MobileNet V2 was exploited for feature extraction. The features of infection were supplied to the SVM classifier for training which produced accurate classification results. The accuracies for CT and X-ray images are 99.42% and 98.54% respectively. The MCC score was used to avoid any mislead caused by accuracy and F1 score as it is more mathematically balanced metric. The MCC scores obtained for CT and X-ray were 0.9852 and 0.9657, respectively. The Youden's index showed a significant improvement of more than 2% for both imaging techniques. The proposed transfer learning-based approach obtained the best results for all evaluation metrics and produced reliable results for the accurate identification of COVID-19 symptoms. This study can help in reducing the time in diagnosis of the infection.

Technology and health care : official journal of the European Society for Engineering and Medicine

10.3233/THC-220114

A Novel Method for COVID-19 Detection Based on DCNNs and Hierarchical Structure.

The worldwide outbreak of the new coronavirus disease (COVID-19) has been declared a pandemic by the World Health Organization (WHO). It has a devastating impact on daily life, public health, and global economy. Due to the highly infectiousness, it is urgent to early screening of suspected cases quickly and accurately. Chest X-ray medical image, as a diagnostic basis for COVID-19, arouses attention from medical engineering. However, due to small lesion difference and lack of training data, the accuracy of detection model is insufficient. In this work, a transfer learning strategy is introduced to hierarchical structure to enhance high-level features of deep convolutional neural networks. The proposed framework consisting of asymmetric pretrained DCNNs with attention networks integrates various information into a wider architecture to learn more discriminative and complementary features. Furthermore, a novel cross-entropy loss function with a penalty term weakens misclassification. Extensive experiments are implemented on the COVID-19 dataset. Compared with the state-of-the-arts, the effectiveness and high performance of the proposed method are demonstrated.

Computational and mathematical methods in medicine

10.1155/2022/2484435 10.1016/j.bbe.2020.08.008 10.1016/j.irbm.2020.05.003 10.1007/s10044-021-00984-y 10.7717/peerj-cs.313 10.1080/07391102.2020.1788642 10.1016/j.compbiomed.2020.103792 10.1101/2020.05.12.20099937 10.1117/12.2581496 10.1155/2022/6185013 10.1016/j.mehy.2020.109761 10.1016/j.patrec.2021.11.020 10.1016/B978-0-12-824536-1.00003-4 10.1007/s10489-020-01900-3 10.1007/s10489-020-01826-w 10.1007/s11517-020-02299-2 10.1016/j.patrec.2020.09.010 10.20944/preprints202005.0151.v1 10.1016/j.bspc.2022.103595 10.1016/j.compbiomed.2021.105134 10.1016/j.bspc.2019.04.031 10.1109/cvpr.2016.90 10.1109/cvpr.2018.00745 10.1109/ACCESS.2020.3010287 10.1186/s40537-019-0197-0 10.1049/ipr2.12090 10.1007/s40846-020-00529-4 10.3390/ijerph18063056 10.3390/healthcare9050522 10.1016/j.irbm.2020.07.001

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks.

Journal of medical imaging (Bellingham, Wash.)

10.1117/1.JMI.9.5.054001 10.1016/j.ajic.2020.07.011 10.1007/s00330-020-07033-y 10.1038/s41598-020-80061-2 10.1148/rg.2020200159 10.1148/radiol.2020200463 10.1148/radiol.2020200843 10.1148/radiol.2020200370 10.1007/s00330-020-06817-6 10.1148/ryct.2020200047 10.1148/ryai.2020200048 10.1148/ryct.2020200441 10.1148/ryct.2020200389 10.1001/archinternmed.2009.440 10.2967/jnumed.120.246256 10.1097/RLU.0000000000003135 10.1016/j.diii.2020.05.011 10.1007/s00259-020-05014-3 10.4103/ijri.IJRI_479_20 10.1016/j.cell.2020.04.045 10.1148/radiol.2020201491 10.1109/TMI.2020.2996645 10.1016/j.media.2020.101836 10.1007/978-3-319-46723-8_49 10.1109/3DV.2016.79 10.1162/neco.1997.9.8.1735 10.1148/radiol.10091808 10.1109/CVPR.2017.243 10.1109/IVS.2019.8813852 10.1109/CVPR.2017.19 10.1109/CVPR.2009.5206848 10.1007/BF02295996 10.1148/radiol.2020200642 10.1148/radiol.2020200905 10.1016/j.metabol.2020.154436 10.1109/TMI.2020.3000314 10.1186/s12880-020-00529-5 10.1155/2020/4706576 10.1007/s00521-020-05514-1 10.1002/int.22586 10.1117/12.2613272

Lung image segmentation based on DRD U-Net and combined WGAN with Deep Neural Network.

COVID-19 is a hot issue right now, and it's causing a huge number of infections in people, posing a grave threat to human life. Deep learning-based image diagnostic technology can effectively enhance the deficiencies of the current main detection method. This paper proposes a multi-classification model diagnosis based on segmentation and classification multi-task. In the segmentation task, the end-to-end DRD U-Net model is used to segment the lung lesions to improve the ability of feature reuse and target segmentation. In the classification task, the model combined with WGAN and Deep Neural Network classifier is used to effectively solve the problem of multi-classification of COVID-19 images with small samples, to achieve the goal of effectively distinguishing COVID-19 patients, other pneumonia patients, and normal subjects. Experiments are carried out on common X-ray image and CT image data sets. The results display that in the segmentation task, the model is optimal in the key indicators of DSC and HD, and the error is increased by 0.33% and reduced by 3.57 mm compared with the original network U-Net. In the classification task, compared with SMOTE oversampling method, accuracy increased from 65.32% to 73.84%, F-measure increased from 67.65% to 74.65%, G-mean increased from 66.52% to 74.37%. At the same time, compared with other classical multi-task models, the results also have some advantages. This study provides new possibilities for COVID-19 image diagnosis methods, improves the accuracy of diagnosis, and hopes to provide substantial help for COVID-19 diagnosis.

Computer methods and programs in biomedicine

10.1016/j.cmpb.2022.107097

Detection of COVID-19 in Point of Care Lung Ultrasound.

The coronavirus disease 2019 (COVID-19) evolved into a global pandemic, responsible for a significant number of infections and deaths. In this scenario, point-of-care ultrasound (POCUS) has emerged as a viable and safe imaging modality. Computer vision (CV) solutions have been proposed to aid clinicians in POCUS image interpretation, namely detection/segmentation of structures and image/patient classification but relevant challenges still remain. As such, the aim of this study is to develop CV algorithms, using Deep Learning techniques, to create tools that can aid doctors in the diagnosis of viral and bacterial pneumonia (VP and BP) through POCUS exams. To do so, convolutional neural networks were designed to perform in classification tasks. The architectures chosen to build these models were the VGG16, ResNet50, DenseNet169 e MobileNetV2. Patients images were divided in three classes: healthy (HE), BP and VP (which includes COVID-19). Through a comparative study, which was based on several performance metrics, the model based on the DenseNet169 architecture was designated as the best performing model, achieving 78% average accuracy value of the five iterations of 5- Fold Cross-Validation. Given that the currently available POCUS datasets for COVID-19 are still limited, the training of the models was negatively affected by such and the models were not tested in an independent dataset. Furthermore, it was also not possible to perform lesion detection tasks. Nonetheless, in order to provide explainability and understanding of the models, Gradient-weighted Class Activation Mapping (GradCAM) were used as a tool to highlight the most relevant classification regions. Clinical relevance - Reveals the potential of POCUS to support COVID-19 screening. The results are very promising although the dataset is limite.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

10.1109/EMBC48229.2022.9871235

Dynamic Classification of Imageless Bioelectrical Impedance Tomography Features with Attention-Driven Spatial Transformer Neural Network.

Point-of-Care monitoring devices have proven to be pivotal in the timely screening and intervention of critical care patients. The urgent demands for their deployment in the COVID-19 pandemic era has translated into the escalation of rapid, reliable, and low-cost monitoring systems research and development. Electrical Impedance Tomography (EIT) is a highly promising modality in providing deep tissue imaging that aids in patient bedside diagnosis and treatment. Motivated to bring forth an accurate and intelligent EIT screening system, we bypassed the complexity and challenges typically associated with its image reconstruction and feature identification processes by solely focusing on the raw data output to extract the embedded knowledge. We developed a novel machine learning architecture based on an attention-driven spatial transformer neural network to specifically accommodate for the patterns and dependencies within EIT raw data. Through elaborate precision-mapped phantom experiments, we validated the reproduction and recognition of features with systemically controlled changes. We demonstrated over 95% accuracy via state-of-the-art machine learning models, and an enhanced performance using our adapted transformer pipeline with shorter training time and greater computational efficiency. Our approach of using imageless EIT driven by a novel attention-focused feature learning algorithm is highly promising in revolutionizing conventional EIT operations and augmenting its practical usage in medicine and beyond.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

10.1109/EMBC48229.2022.9870921

Transfer Learning for Automated COVID-19 B-Line Classification in Lung Ultrasound.

Lung ultrasound (LUS) as a diagnostic tool is gaining support for its role in the diagnosis and management of COVID-19 and a number of other lung pathologies. B-lines are a predominant feature in COVID-19, however LUS requires a skilled clinician to interpret findings. To facilitate the interpretation, our main objective was to develop automated methods to classify B-lines as pathologic vs. normal. We developed transfer learning models based on ResNet networks to classify B-lines as pathologic (at least 3 B-lines per lung field) vs. normal using COVID-19 LUS data. Assessment of B-line severity on a 0-4 multi-class scale was also explored. For binary B-line classification, at the frame-level, all ResNet models pretrained with ImageNet yielded higher performance than the baseline nonpretrained ResNet-18. Pretrained ResNet-18 has the best Equal Error Rate (EER) of 9.1% vs the baseline of 11.9%. At the clip-level, all pretrained network models resulted in better Cohen's kappa agreement (linear-weighted) and clip score accuracy, with the pretrained ResNet-18 having the best Cohen's kappa of 0.815 [95% CI: 0.804-0.826], and ResNet-101 the best clip scoring accuracy of 93.6%. Similar results were shown for multi-class scoring, where pretrained network models outperformed the baseline model. A class activation map is also presented to guide clinicians in interpreting LUS findings. Future work aims to further improve the multi-class assessment for severity of B-lines with a more diverse LUS dataset.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

10.1109/EMBC48229.2022.9871894

Wasserstein GAN based Chest X-Ray Dataset Augmentation for Deep Learning Models: COVID-19 Detection Use-Case.

The novel coronavirus infection (COVID-19) is still continuing to be a concern for the entire globe. Since early detection of COVID-19 is of particular importance, there have been multiple research efforts to supplement the current standard RT-PCR tests. Several deep learning models, with varying effectiveness, using Chest X-Ray images for such diagnosis have also been proposed. While some of the models are quite promising, there still remains a dearth of training data for such deep learning models. The present paper attempts to provide a viable solution to the problem of data deficiency in COVID-19 CXR images. We show that the use of a Wasserstein Generative Adversarial Network (WGAN) could lead to an effective and lightweight solution. It is demonstrated that the WGAN generated images are at par with the original images using inference tests on an already proposed COVID-19 detection model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

10.1109/EMBC48229.2022.9871519

Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation.

Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high resource utilisation. However, deep learning models are not trusted in the clinical routine due to failing silently on out-of-distribution (OOD) data. We propose a lightweight OOD detection method that leverages the Mahalanobis distance in the feature space and seamlessly integrates into state-of-the-art segmentation pipelines. The simple approach can even augment pre-trained models with clinically relevant uncertainty quantification. We validate our method across four chest CT distribution shifts and two magnetic resonance imaging applications, namely segmentation of the hippocampus and the prostate. Our results show that the proposed method effectively detects far- and near-OOD samples across all explored scenarios.

Medical image analysis

10.1016/j.media.2022.102596 10.7937/K9/TCIA.2015.zF0vlOPv 10.5281/zenodo.3757476 10.1016/j.cmpb.2021.106236 10.1055/a-1544-2240

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention.

Aiming at detecting COVID-19 effectively, a multiscale class residual attention (MCRA) network is proposed via chest X-ray (CXR) image classification. First, to overcome the data shortage and improve the robustness of our network, a pixel-level image mixing of local regions was introduced to achieve data augmentation and reduce noise. Secondly, multi-scale fusion strategy was adopted to extract global contextual information at different scales and enhance semantic representation. Last but not least, class residual attention was employed to generate spatial attention for each class, which can avoid inter-class interference and enhance related features to further improve the COVID-19 detection. Experimental results show that our network achieves superior diagnostic performance on COVIDx dataset, and its accuracy, PPV, sensitivity, specificity and F1-score are 97.71%, 96.76%, 96.56%, 98.96% and 96.64%, respectively; moreover, the heat maps can endow our deep model with somewhat interpretability.

Computers in biology and medicine

10.1016/j.compbiomed.2022.106065 10.1016/j.compbiomed.2022.105350 10.1109/TIP.2021.3058783 10.1007/s00330-020-07268-9 10.1109/TNNLS.2021.3086570 10.1109/TBDATA.2017.2717439 10.1007/s11063-021-10569-9 10.32604/cmes.2020.09463 10.1016/j.micpro.2020.103282 10.2174/1574893615666200207094357 10.1016/j.compbiomed.2022.105383 10.1007/s10489-021-02393-4 10.1109/TMI.2021.3117564 10.1016/j.asoc.2021.108041 10.1016/j.compbiomed.2021.105002 10.1016/j.media.2020.101839 10.1007/s11063-022-10742-8 10.1016/j.compbiomed.2021.105127 10.1007/s10489-021-02572-3 10.1109/TMI.2021.3127074 10.1007/s00521-021-06806-w 10.1016/j.compbiomed.2022.105604 10.1016/j.compbiomed.2022.105244 10.1016/j.compbiomed.2022.105210 10.1007/s10489-021-02691-x 10.1016/j.compbiomed.2022.105335 10.1109/TIM.2021.3128703 10.1109/TGRS.2021.3080580 10.1109/TIP.2021.3124668 10.1109/TIP.2021.3127851 10.1109/TGRS.2021.3056624 10.1016/j.neucom.2021.12.077 10.1109/TIP.2022.3144017 10.1109/TMI.2021.3140120 10.1016/j.media.2021.102345 10.1109/TIP.2021.3139232 10.1109/TIP.2022.3154931 10.1016/j.neucom.2021.11.104 10.1016/j.media.2022.102381 10.1109/TPAMI.2020.3040258 10.1109/TPAMI.2020.3026069 10.1109/CVPR.2016.90

Semantic-Powered Explainable Model-Free Few-Shot Learning Scheme of Diagnosing COVID-19 on Chest X-Ray.

Chest X-ray (CXR) is commonly performed as an initial investigation in COVID-19, whose fast and accurate diagnosis is critical. Recently, deep learning has a great potential in detecting people who are suspected to be infected with COVID-19. However, deep learning resulting with black-box models, which often breaks down when forced to make predictions about data for which limited supervised information is available and lack inter-pretability, still is a major barrier for clinical integration. In this work, we hereby propose a semantic-powered explainable model-free few-shot learning scheme to quickly and precisely diagnose COVID-19 with higher reliability and transparency. Specifically, we design a Report Image Explanation Cell (RIEC) to exploit clinically indicators derived from radiology reports as interpretable driver to introduce prior knowledge at training. Meanwhile, multi-task collaborative diagnosis strategy (MCDS) is developed to construct N-way K-shot tasks, which adopts a cyclic and collaborative training approach for producing better generalization performance on new tasks. Extensive experiments demonstrate that the proposed scheme achieves competitive results (accuracy of 98.91%, precision of 98.95%, recall of 97.94% and F1-score of 98.57%) to diagnose COVID-19 and other pneumonia infected categories, even with only 200 paired CXR images and radiology reports for training. Furthermore, statistical results of comparative experiments show that our scheme provides an interpretable window into the COVID-19 diagnosis to improve the performance of the small sample size, the reliability and transparency of black-box deep learning models. Our source codes will be released on https://github.com/AI-medical-diagnosis-team-of-JNU/SPEMFSL-Diagnosis-COVID-19.

IEEE journal of biomedical and health informatics

10.1109/JBHI.2022.3205167

Ensemble of Deep Neural Networks based on Condorcet's Jury Theorem for screening Covid-19 and Pneumonia from radiograph images.

COVID-19 detection using Artificial Intelligence and Computer-Aided Diagnosis has been the subject of several studies. Deep Neural Networks with hundreds or even millions of parameters (weights) are referred to as "black boxes" because their behavior is difficult to comprehend, even when the model's structure and weights are visible. On the same dataset, different Deep Convolutional Neural Networks perform differently. So, we do not necessarily have to rely on just one model; instead, we can evaluate our final score by combining multiple models. While including multiple models in the voter pool, it is not always true that the accuracy will improve. So, In this regard, the authors proposed a novel approach to determine the voting ensemble score of individual classifiers based on Condorcet's Jury Theorem (CJT). The authors demonstrated that the theorem holds while ensembling the N number of classifiers in Neural Networks. With the help of CJT, the authors proved that a model's presence in the voter pool would improve the likelihood that the majority vote will be accurate if it is more accurate than the other models. Besides this, the authors also proposed a Domain Extended Transfer Learning (DETL) ensemble model as a soft voting ensemble method and compared it with CJT based ensemble method. Furthermore, as deep learning models typically fail in real-world testing, a novel dataset has been used with no duplicate images. Duplicates in the dataset are quite problematic since they might affect the training process. Therefore, having a dataset devoid of duplicate images is considered to prevent data leakage problems that might impede the thorough assessment of the trained models. The authors also employed an algorithm for faster training to save computational efforts. Our proposed method and experimental results outperformed the state-of-the-art with the DETL-based ensemble model showing an accuracy of 97.26%, COVID-19, sensitivity of 98.37%, and specificity of 100%. CJT-based ensemble model showed an accuracy of 98.22%, COVID-19, sensitivity of 98.37%, and specificity of 99.79%.

Computers in biology and medicine

10.1016/j.compbiomed.2022.105979 10.1109/TII.2021.3057683 10.1109/TII.2021.3057524

Deep learning framework for prediction of infection severity of COVID-19.

With the onset of the COVID-19 pandemic, quantifying the condition of positively diagnosed patients is of paramount importance. Chest CT scans can be used to measure the severity of a lung infection and the isolate involvement sites in order to increase awareness of a patient's disease progression. In this work, we developed a deep learning framework for lung infection severity prediction. To this end, we collected a dataset of 232 chest CT scans and involved two public datasets with an additional 59 scans for our model's training and used two external test sets with 21 scans for evaluation. On an input chest Computer Tomography (CT) scan, our framework, in parallel, performs a lung lobe segmentation utilizing a pre-trained model and infection segmentation using three distinct trained

Frontiers in medicine

10.3389/fmed.2022.940960 10.1016/j.idm.2020.02.002 10.1101/2020.02.27.20028027 10.1101/2020.02.07.937862 10.1148/radiol.2020200642 10.1148/radiol.2020200343 10.1371/journal.pone.0250952 10.1038/nature14539 10.1038/s41598-019-51503-3 10.1038/s41591-019-0447-x 10.1038/s41598-019-56589-3 10.1109/TNNLS.2019.2892409 10.1038/s41591-020-0931-3 10.1016/j.patcog.2018.07.031 10.1016/j.dsx.2020.04.012 10.1109/RBME.2020.2987975 10.1016/S2589-7500(20)30054-6 10.1148/ryct.2020200075 10.1101/2020.03.24.20041020 10.1136/bmj.m1328 10.1007/s42979-020-00216-w 10.1007/s10916-020-01582-x 10.1109/TAI.2020.3020521 10.1007/s10278-019-00227-x 10.1038/srep46479 10.1109/JBHI.2017.2725903 10.1002/mp.14676 10.1007/s00330-020-07042-x 10.1016/j.knosys.2020.106647 10.1109/TMI.2020.2995108 10.1109/ISBI.2019.8759468 10.1007/s10278-019-00223-1 10.1186/s41747-020-00173-2 10.1038/s41598-020-76282-0 10.48550/arXiv.2003.11988 10.1007/s10489-020-01829-7 10.1016/j.patcog.2020.107747 10.3389/fpubh.2020.00357 10.48550/arXiv.2006.05018 10.1101/2020.05.20.20108159 10.1101/2020.05.20.20100362 10.1007/978-3-319-24574-4_28 10.48550/arXiv.1511.07122 10.1109/TPAMI.2017.2699184 10.1118/1.3611983 10.1016/j.imu.2022.100935

A novel adaptive cubic quasi-Newton optimizer for deep learning based medical image analysis tasks, validated on detection of COVID-19 and segmentation for COVID-19 lung infection, liver tumor, and optic disc/cup.

Most of existing deep learning research in medical image analysis is focused on networks with stronger performance. These networks have achieved success, while their architectures are complex and even contain massive parameters ranging from thousands to millions in numbers. The nature of high dimension and nonconvex makes it easy to train a suboptimal model through the popular stochastic first-order optimizers, which only use gradient information. Our purpose is to design an adaptive cubic quasi-Newton optimizer, which could help to escape from suboptimal solution and improve the performance of deep neural networks on four medical image analysis tasks including: detection of COVID-19, COVID-19 lung infection segmentation, liver tumor segmentation, optic disc/cup segmentation. In this work, we introduce a novel adaptive cubic quasi-Newton optimizer with high-order moment (termed ACQN-H) for medical image analysis. The optimizer dynamically captures the curvature of the loss function by diagonally approximated Hessian and the norm of difference between previous two estimates, which helps to escape from saddle points more efficiently. In addition, to reduce the variance introduced by the stochastic nature of the problem, ACQN-H hires high-order moment through exponential moving average on iteratively calculated approximated Hessian matrix. Extensive experiments are performed to access the performance of ACQN-H. These include detection of COVID-19 using COVID-Net on dataset COVID-chestxray, which contains 16 565 training samples and 1841 test samples; COVID-19 lung infection segmentation using Inf-Net on COVID-CT, which contains 45, 5, and 5 computer tomography (CT) images for training, validation, and testing, respectively; liver tumor segmentation using ResUNet on LiTS2017, which consists of 50 622 abdominal scan images for training and 26 608 images for testing; optic disc/cup segmentation using MRNet on RIGA, which has 655 color fundus images for training and 95 for testing. The results are compared with commonly used stochastic first-order optimizers such as Adam, SGD, and AdaBound, and recently proposed stochastic quasi-Newton optimizer Apollo. In task detection of COVID-19, we use classification accuracy as the evaluation metric. For the other three medical image segmentation tasks, seven commonly used evaluation metrics are utilized, that is, Dice, structure measure, enhanced-alignment measure (EM), mean absolute error (MAE), intersection over union (IoU), true positive rate (TPR), and true negative rate. Experiments on four tasks show that ACQN-H achieves improvements over other stochastic optimizers: (1) comparing with AdaBound, ACQN-H achieves 0.49%, 0.11%, and 0.70% higher accuracy on the COVID-chestxray dataset using network COVID-Net with VGG16, ResNet50 and DenseNet121 as backbones, respectively; (2) ACQN-H has the best scores in terms of evaluation metrics Dice, TPR, EM, and MAE on COVID-CT dataset using network Inf-Net. Particularly, ACQN-H achieves 1.0% better Dice as compared to Apollo; (3) ACQN-H achieves the best results on LiTS2017 dataset using network ResUNet, and outperforms Adam in terms of Dice by 2.3%; (4) ACQN-H improves the performance of network MRNet on RIGA dataset, and achieves 0.5% and 1.0% better scores on cup segmentation for Dice and IoU, respectively, compared with SGD. We also present fivefold validation results of four tasks. It can be found that the results on detection of COVID-19, liver tumor segmentation and optic disc/cup segmentation can achieve high performance with low variance. For COVID-19 lung infection segmentation, the variance on test set is much larger than on validation set, which may due to small size of dataset. The proposed optimizer ACQN-H has been validated on four medical image analysis tasks including: detection of COVID-19 using COVID-Net on COVID-chestxray, COVID-19 lung infection segmentation using Inf-Net on COVID-CT, liver tumor segmentation using ResUNet on LiTS2017, optic disc/cup segmentation using MRNet on RIGA. Experiments show that ACQN-H can achieve some performance improvement. Moreover, the work is expected to boost the performance of existing deep learning networks in medical image analysis.

Medical physics

10.1002/mp.15969

Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity.

As an epidemic, COVID-19's core test instrument still has serious flaws. To improve the present condition, all capabilities and tools available in this field are being used to combat the pandemic. Because of the contagious characteristics of the unique coronavirus (COVID-19) infection, an overwhelming comparison with patients queues up for pulmonary X-rays, overloading physicians and radiology and significantly impacting the quality of care, diagnosis, and outbreak prevention. Given the scarcity of clinical services such as intensive care and motorized ventilation systems in the aspect of this vastly transmissible ailment, it is critical to categorize patients as per their risk categories. This research describes a novel use of the deep convolutional neural network (CNN) technique to COVID-19 illness assessment seriousness. Utilizing chest X-ray images as contribution, an unsupervised DCNN model is constructed and suggested to split COVID-19 individuals into four seriousness classrooms: low, medium, serious, and crucial with an accuracy level of 96 percent. The efficiency of the DCNN model developed with the proposed methodology is demonstrated by empirical findings on a suitably huge sum of chest X-ray scans. To the evidence relating, it is the first COVID-19 disease incidence evaluation research with four different phases, to use a reasonably high number of X-ray images dataset and a DCNN with nearly all hyperparameters dynamically adjusted by the variable selection optimization task.

BioMed research international

10.1155/2022/1289221 10.1109/ISMSIT50672.2020.9255149 10.3390/electronics10141677 10.1101/2020.06.25.20140004 10.1002/pa.2537 10.1016/j.asoc.2020.106912 10.1109/SMART-TECH49988.2020.00041 10.1155/2022/4352730 10.3390/a13100249 10.1016/j.idm.2020.03.002 10.1155/2021/5709257 10.1007/s42600-020-00105-4 10.4066/biomedicalresearch.29-18-886 10.1007/s40747-021-00312-1 10.1155/2020/8856801 10.1007/s10489-020-01829-7 10.1155/2021/6927985 10.1155/2021/5587188 10.1016/j.chaos.2020.110056 10.3390/jpm11050343 10.3390/jcm9061668 10.1016/j.cmpb.2019.06.023 10.3390/info12030109 10.1016/j.chaos.2020.110059 10.1016/j.ipm.2021.102809 10.1371/journal.pone.0241332 10.3389/fimmu.2020.01581 10.1109/ACCESS.2020.2997311 10.1016/j.patter.2020.100074 10.1148/radiol.2021204531 10.1016/S2589-7500(20)30162-X

Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data.

With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical datasets became a key factor to enable reproducible development of diagnostic algorithms in the medical domain. Medical data contains sensitive patient-related information and is therefore usually anonymized by removing patient identifiers, e.g., patient names before publication. To the best of our knowledge, we are the first to show that a well-trained deep learning system is able to recover the patient identity from chest X-ray data. We demonstrate this using the publicly available large-scale ChestX-ray14 dataset, a collection of 112,120 frontal-view chest X-ray images from 30,805 unique patients. Our verification system is able to identify whether two frontal chest X-ray images are from the same person with an AUC of 0.9940 and a classification accuracy of 95.55%. We further highlight that the proposed system is able to reveal the same person even ten and more years after the initial scan. When pursuing a retrieval approach, we observe an mAP@R of 0.9748 and a precision@1 of 0.9963. Furthermore, we achieve an AUC of up to 0.9870 and a precision@1 of up to 0.9444 when evaluating our trained networks on external datasets such as CheXpert and the COVID-19 Image Data Collection. Based on this high identification rate, a potential attacker may leak patient-related information and additionally cross-reference images to obtain more information. Thus, there is a great risk of sensitive content falling into unauthorized hands or being disseminated against the will of the concerned patients. Especially during the COVID-19 pandemic, numerous chest X-ray datasets have been published to advance research. Therefore, such data may be vulnerable to potential attacks by deep learning-based re-identification algorithms.

Scientific reports

10.1038/s41598-022-19045-3 10.1378/chest.10-1302 10.1038/s41598-019-56847-4 10.2214/AJR.12.10375 10.1038/nature14539 10.1016/j.zemedi.2018.12.003 10.1016/j.acra.2019.10.006 10.1016/S0197-2456(00)00097-0 10.1007/s40256-020-00420-2 10.1148/radiol.2020192224 10.1007/s10278-006-1051-4 10.1142/S0218488502001648 10.1016/j.csl.2019.06.001 10.1145/1866739.1866758 10.1561/0400000042 10.1038/s42256-020-0186-1 10.1109/MSP.2013.2259911 10.1038/s41746-020-00323-1 10.1038/s42256-021-00337-8 10.1126/science.aab3050 10.1109/5.726791 10.1016/j.patrec.2005.10.010

Point-of-care SARS-CoV-2 sensing using lens-free imaging and a deep learning-assisted quantitative agglutination assay.

The persistence of the global COVID-19 pandemic caused by the SARS-CoV-2 virus has continued to emphasize the need for point-of-care (POC) diagnostic tests for viral diagnosis. The most widely used tests, lateral flow assays used in rapid antigen tests, and reverse-transcriptase real-time polymerase chain reaction (RT-PCR), have been instrumental in mitigating the impact of new waves of the pandemic, but fail to provide both sensitive and rapid readout to patients. Here, we present a portable lens-free imaging system coupled with a particle agglutination assay as a novel biosensor for SARS-CoV-2. This sensor images and quantifies individual microbeads undergoing agglutination through a combination of computational imaging and deep learning as a way to detect levels of SARS-CoV-2 in a complex sample. SARS-CoV-2 pseudovirus in solution is incubated with acetyl cholinesterase 2 (ACE2)-functionalized microbeads then loaded into an inexpensive imaging chip. The sample is imaged in a portable in-line lens-free holographic microscope and an image is reconstructed from a pixel superresolved hologram. Images are analyzed by a deep-learning algorithm that distinguishes microbead agglutination from cell debris and viral particle aggregates, and agglutination is quantified based on the network output. We propose an assay procedure using two images which results in the accurate determination of viral concentrations greater than the limit of detection (LOD) of 1.27 × 10

Lab on a chip

10.1039/d2lc00289b

Automated COVID-19 Classification Using Heap-Based Optimization with the Deep Transfer Learning Model.

The outbreak of the COVID-19 pandemic necessitates prompt identification of affected persons to restrict the spread of the COVID-19 epidemic. Radiological imaging such as computed tomography (CT) and chest X-rays (CXR) is considered an effective way to diagnose COVID-19. However, it needs an expert's knowledge and consumes more time. At the same time, artificial intelligence (AI) and medical images are discovered to be helpful in effectively assessing and providing treatment for COVID-19 infected patients. In particular, deep learning (DL) models act as a vital part of a high-performance classification model for COVID-19 recognition on CXR images. This study develops a heap-based optimization with the deep transfer learning model for detection and classification (HBODTL-DC) of COVID-19. The proposed HBODTL-DC system majorly focuses on the identification of COVID-19 on CXR images. To do so, the presented HBODTL-DC model initially exploits the Gabor filtering (GF) technique to enhance the image quality. In addition, the HBO algorithm with a neural architecture search network (NasNet) large model is employed for the extraction of feature vectors. Finally, Elman Neural Network (ENN) model gets the feature vectors as input and categorizes the CXR images into distinct classes. The experimental validation of the HBODTL-DC model takes place on the benchmark CXR image dataset from the Kaggle repository, and the outcomes are checked in numerous dimensions. The experimental outcomes stated the supremacy of the HBODTL-DC model over recent approaches with a maximum accuracy of 0.9992.

Computational intelligence and neuroscience

10.1155/2022/7508836 10.3390/jpm12020309 10.3390/s21217286 10.3390/ijerph18063056 10.1016/j.patrec.2021.08.018 10.3390/app11199023 10.1016/j.bbe.2020.08.008 10.1155/2020/8828855 10.1016/j.eswa.2020.114054 10.1007/s11760-020-01820-2 10.1016/j.cmpb.2020.105581 10.1109/SSCI47803.2020.9308571 10.3390/s21041480 10.1109/access.2020.3025010 10.3390/diagnostics11050895 10.1016/j.patcog.2014.01.006 10.1016/j.eswa.2020.113702 10.1016/j.asej.2022.101728 10.1177/0361198120967943 10.1016/j.compbiomed.2021.104816 10.1155/2022/6074538 10.1155/2022/6185013

COV-RadNet: A Deep Convolutional Neural Network for Automatic Detection of COVID-19 from Chest X-Rays and CT Scans.

With the increase in severity of COVID-19 pandemic situation, the world is facing a critical fight to cope up with the impacts on human health, education and economy. The ongoing battle with the novel corona virus, is showing much priority to diagnose and provide rapid treatment to the patients. The rapid growth of COVID-19 has broken the healthcare system of the affected countries, creating a shortage in ICUs, test kits, ventilation support system. etc. This paper aims at finding an automatic COVID-19 detection approach which will assist the medical practitioners to diagnose the disease quickly and effectively. In this paper, a deep convolutional neural network, 'COV-RadNet' is proposed to detect COVID positive, viral pneumonia, lung opacity and normal, healthy people by analyzing their Chest Radiographic (X-ray and CT scans) images. Data augmentation technique is applied to balance the dataset 'COVID 19 Radiography Dataset' to make the classifier more robust to the classification task. We have applied transfer learning approach using four deep learning based models: VGG16, VGG19, ResNet152 and ResNext 101 to detect COVID-19 from chest X-ray images. We have achieved 97% classification accuracy using our proposed COV-RadNet model for COVID/Viral Pneumonia/Lungs Opacity/Normal, 99.5% accuracy to detect COVID/Viral Pneumonia/Normal and 99.72% accuracy to detect COVID and non-COVID people. Using chest CT scan images, we have found 99.25% accuracy to classify between COVID and non-COVID classes. Among the performance of the pre-trained models, ResNext 101 has shown the highest accuracy of 98.5% for multiclass classification (COVID, viral pneumonia, Lungs opacity and normal).

Computer methods and programs in biomedicine update

10.1016/j.cmpbup.2022.100064

Chest X-ray analysis empowered with deep learning: A systematic review.

Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyze chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research.

Applied soft computing

10.1016/j.asoc.2022.109319 10.1038/s41392-020-00243-2 10.1148/ryct.2020200028 10.1016/j.compmedimag.2019.05.005 10.1109/42.974918 10.1109/ACCESS.2021.3065965 10.3390/app10020559 10.1007/s13246-020-00865-4 10.1016/B978-0-12-819061-6.00013-6 10.1007/s11633-020-1231-6 10.3390/jimaging6120131 10.1016/j.media.2021.102125 10.30534/ijeter/2021/09972021 10.1016/j.scs.2020.102589 10.1109/MCI.2020.3019873 10.1109/ic-ETITE47903.2020.152 10.1016/j.compbiomed.2020.103898 10.1097/01.NAJ.0000444496.24228.2c 10.1016/j.chaos.2020.110337 10.1007/978-981-15-7219-7_22 10.1109/EMBC44109.2020.9175594 10.3390/diagnostics10060417 10.1109/CVPR.2016.90 10.1109/CVPR.2017.243 10.48550/arXiv.1602.07360 10.1109/CVPR.2017.195 10.1007/978-3-319-24574-4_28 10.1016/j.compmedimag.2017.04.001 10.1016/j.asoc.2020.106580 10.1016/j.asoc.2020.106691 10.1017/9781139061773 10.1109/IES50839.2020.9231540 10.1109/ICOSEC49089.2020.9215257 10.1145/3431804 10.1109/KSE.2018.8573404 10.1038/s41598-020-76550-z 10.1016/j.chaos.2020.109944 10.1109/INDIACom51348.2021.00137 10.1109/ICCCNT49239.2020.9225543 10.1109/ISRITI51436.2020.9315478 10.1007/s10044-021-00970-4 10.1155/2021/5513679 10.1002/ima.22566 10.1016/j.compbiomed.2020.103805 10.1016/j.knosys.2020.106062 10.1117/12.2547635 10.1109/TMI.2013.2290491 10.1109/TMI.2013.2284099 10.1007/978-3-030-32254-0_74 10.1109/ICVEE50212.2020.9243290 10.1109/EMBC44109.2020.9176517 10.1109/EBBT.2019.8741582 10.1007/s00264-020-04609-7 10.1007/s10489-020-01829-7 10.1016/j.imu.2020.100405 10.1109/ICECOCS50124.2020.9314567 10.1016/j.jjimei.2021.100020 10.1101/2020.03.26.20044610 10.1109/ACCESS.2021.3086229 10.1117/12.2581314 10.1109/RIVF48685.2020.9140733 10.1109/ACCESS.2020.2974242 10.31661/jbpe.v0i0.2008-1153 10.1109/ICISS49785.2020.9316100 10.1109/DASA53625.2021.9682248 10.48550/arXiv.1711.05225 10.1109/ICECCT.2019.8869364 10.1016/j.chaos.2020.110122 10.1016/j.patrec.2020.09.010 10.1007/s42600-021-00151-6 10.1016/j.eswa.2021.114883 10.1016/j.mehy.2020.109761 10.1109/ICECCE49384.2020.9179404 10.1155/2020/8828855 10.48550/arXiv.1409.1556 10.1109/CVPR.2018.00474 10.5614/itbj.ict.res.appl.2019.13.3.5 10.1117/12.2581882 10.17632/rscbjbr9sj.3 10.1155/2019/4180949 10.5220/0007404301120119 10.1007/s12559-020-09795-5 10.1016/j.cmpb.2020.105581 10.1177/2472630320958376 10.3390/s21041480 10.1109/ACCESS.2020.3010287 10.1016/j.compbiomed.2021.104319 10.1016/j.chaos.2020.110245 10.1109/Confluence47617.2020.9057809 10.1109/CCECE.2019.8861969 10.1007/978-981-15-3369-3_36 10.1142/S0218001421510046 10.1109/EBBT.2019.8742050 10.34740/kaggle/dsv/1019469 10.17632/2fxz4px6d8.4 10.1016/j.compbiomed.2020.103792 10.1016/j.measurement.2019.05.076 10.1109/EIT48999.2020.9208232 10.12928/TELKOMNIKA.v18i3.14751 10.5220/0007346600760083 10.32604/cmc.2021.018514 10.1016/j.sysarc.2019.101635 10.1007/978-3-642-21219-2_1 10.1007/978-3-030-32248-9_45 10.1109/ICARC54489.2022.9753811 10.3991/ijoe.v18i07.30807 10.48550/arXiv.1701.03757 10.1016/j.simpa.2022.100340 10.1148/ryai.2020190043 10.1007/s00521-021-06396-7

SEL-COVIDNET: An intelligent application for the diagnosis of COVID-19 from chest X-rays and CT-scans.

COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). We include a global average pooling layer, flattening, and two dense layers that are fully connected. The model's effectiveness is evaluated using balanced and unbalanced COVID-19 radiography datasets. After that, our model's performance is analyzed using six evaluation measures: accuracy, sensitivity, specificity, precision, F1-score, and Matthew's correlation coefficient (MCC). Experiments demonstrated that the proposed SEL-COVIDNET with tuned DenseNet121, InceptionResNetV2, and MobileNetV3Large models outperformed the results of comparative SOTA for multi-class classification (COVID-19 vs. No-finding vs. Pneumonia) in terms of accuracy (98.52%), specificity (98.5%), sensitivity (98.5%), precision (98.7%), F1-score (98.7%), and MCC (97.5%). For the COVID-19 vs. No-finding classification, our method had an accuracy of 99.77%, a specificity of 99.85%, a sensitivity of 99.85%, a precision of 99.55%, an F1-score of 99.7%, and an MCC of 99.4%. The proposed model offers an accurate approach for detecting COVID-19 patients, which aids in the containment of the COVID-19 pandemic.

Informatics in medicine unlocked

10.1016/j.imu.2022.101059 10.1145/3447450.3447458 10.1109/ACCESS.2020.3010287 10.48550/ARXIV.1512.03385 10.48550/ARXIV.1801.04381 10.1109/ICCV.2019.00140 10.1109/CVPRW50498.2020.00183

HADCNet: Automatic segmentation of COVID-19 infection based on a hybrid attention dense connected network with dilated convolution.

the automatic segmentation of lung infections in CT slices provides a rapid and effective strategy for diagnosing, treating, and assessing COVID-19 cases. However, the segmentation of the infected areas presents several difficulties, including high intraclass variability and interclass similarity among infected areas, as well as blurred edges and low contrast. Therefore, we propose HADCNet, a deep learning framework that segments lung infections based on a dual hybrid attention strategy. HADCNet uses an encoder hybrid attention module to integrate feature information at different scales across the peer hierarchy to refine the feature map. Furthermore, a decoder hybrid attention module uses an improved skip connection to embed the semantic information of higher-level features into lower-level features by integrating multi-scale contextual structures and assigning the spatial information of lower-level features to higher-level features, thereby capturing the contextual dependencies of lesion features across levels and refining the semantic structure, which reduces the semantic gap between feature maps at different levels and improves the model segmentation performance. We conducted fivefold cross-validations of our model on four publicly available datasets, with final mean Dice scores of 0.792, 0.796, 0.785, and 0.723. These results show that the proposed model outperforms popular state-of-the-art semantic segmentation methods and indicate its potential use in the diagnosis and treatment of COVID-19.

Computers in biology and medicine

10.1016/j.compbiomed.2022.105981

Novel Coronavirus and Common Pneumonia Detection from CT Scans Using Deep Learning-Based Extracted Features.

COVID-19 which was announced as a pandemic on 11 March 2020, is still infecting millions to date as the vaccines that have been developed do not prevent the disease but rather reduce the severity of the symptoms. Until a vaccine is developed that can prevent COVID-19 infection, the testing of individuals will be a continuous process. Medical personnel monitor and treat all health conditions; hence, the time-consuming process to monitor and test all individuals for COVID-19 becomes an impossible task, especially as COVID-19 shares similar symptoms with the common cold and pneumonia. Some off-the-counter tests have been developed and sold, but they are unreliable and add an additional burden because false-positive cases have to visit hospitals and perform specialized diagnostic tests to confirm the diagnosis. Therefore, the need for systems that can automatically detect and diagnose COVID-19 automatically without human intervention is still an urgent priority and will remain so because the same technology can be used for future pandemics and other health conditions. In this paper, we propose a modified machine learning (ML) process that integrates deep learning (DL) algorithms for feature extraction and well-known classifiers that can accurately detect and diagnose COVID-19 from chest CT scans. Publicly available datasets were made available by the China Consortium for Chest CT Image Investigation (CC-CCII). The highest average accuracy obtained was 99.9% using the modified ML process when 2000 features were extracted using GoogleNet and ResNet18 and using the support vector machine (SVM) classifier. The results obtained using the modified ML process were higher when compared to similar methods reported in the extant literature using the same datasets or different datasets of similar size; thus, this study is considered of added value to the current body of knowledge. Further research in this field is required to develop methods that can be applied in hospitals and can better equip mankind to be prepared for any future pandemics.

Viruses

10.3390/v14081667 10.1007/s10044-021-00984-y 10.1016/j.idm.2020.02.002 10.3201/eid1212.060401 10.1136/bmj.m641 10.1001/jama.2020.2565 10.1016/j.dsx.2020.04.012 10.1109/JBHI.2020.3037127 10.1016/j.media.2020.101797 10.1007/s10489-020-02029-z 10.1016/j.susoc.2021.08.001 10.1109/ACCESS.2020.3016780 10.1016/j.eswa.2020.114054 10.1109/ACCESS.2020.2994762 10.1016/j.chaos.2020.110495 10.1007/s10489-020-01902-1 10.3390/s21020455 10.3390/s21041480 10.3390/diagnostics11111972 10.1016/j.compbiomed.2022.105233 10.1007/s11042-022-11913-4 10.2174/1573405614666180402150218 10.1016/j.procs.2019.12.110 10.1155/2022/2665283 10.3390/math10050796 10.1002/mp.14609 10.3390/diagnostics11071155 10.1016/j.jcp.2020.110010 10.3233/JIFS-189132 10.1109/ictcs.2019.8923092 10.3390/app10134523 10.1007/s40846-021-00656-6 10.2139/ssrn.3754116 10.4316/AECE.2014.01010 10.1145/3277104.3278311 10.3390/diagnostics12041018 10.1007/978-1-4471-7296-3_21 10.1016/j.cell.2020.04.045 10.1016/j.media.2020.101913 10.1002/mp.15044 10.1016/j.compbiomed.2021.104857

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report.

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

Journal of cardiovascular development and disease

10.3390/jcdd9080268 10.1007/s10072-021-05756-4 10.3233/JPD-202038 10.3389/fpsyt.2020.590134 10.1001/jama.2020.1585 10.1016/j.compbiomed.2020.103960 10.1186/s13054-020-03062-7 10.1161/CIRCULATIONAHA.112.093245 10.1161/01.ATV.0000051384.43104.FC 10.1016/S0140-6736(20)30937-5 10.1038/s41577-020-0343-0 10.1016/j.atherosclerosis.2020.10.014 10.1016/j.cell.2020.04.004 10.21037/cdt-20-561 10.1016/j.ejrad.2019.02.038 10.1007/s10916-017-0797-1 10.1016/j.compbiomed.2020.103804 10.1007/s10554-020-02099-7 10.1007/s00296-020-04691-5 10.4239/wjd.v12.i3.215 10.1007/s11517-018-1897-x 10.1016/j.ultras.2011.11.003 10.7863/ultra.33.2.245 10.1007/s10278-012-9553-8 10.3109/02652048.2013.879932 10.1016/j.cmpb.2017.07.011 10.1016/j.eswa.2015.03.014 10.1109/TIM.2011.2174897 10.1007/s10916-017-0745-0 10.1007/s11517-006-0119-0 10.1016/j.cmpb.2016.02.004 10.21037/cdt.2016.03.08 10.3390/diagnostics11122367 10.3390/jcm9072146 10.1007/s11517-012-1019-0 10.1016/j.cmpb.2012.09.008 10.1007/s11883-018-0736-8 10.3390/diagnostics11112109 10.1016/j.compbiomed.2021.105131 10.1007/s10916-021-01707-w 10.1109/JBHI.2021.3103839 10.1016/j.mehy.2020.109603 10.1007/s13721-017-0155-8 10.3389/fnagi.2021.633752 10.1016/j.virol.2015.03.043 10.1038/s41579-018-0118-9 10.1038/nrmicro2090 10.1001/jama.2020.6019 10.1038/d41573-020-00016-0 10.1056/NEJMoa2001282 10.1086/375233 10.1001/jama.2020.2648 10.1038/nri2171 10.1161/CIRCULATIONAHA.104.510461 10.1152/ajplung.00498.2016 10.1016/j.cell.2020.02.058 10.1038/s41598-022-07918-6 10.1177/03009858221079665 10.1007/s12250-020-00207-4 10.1128/JVI.01248-09 10.1002/rth2.12175 10.2147/COPD.S329783 10.1128/mBio.00638-15 10.1016/S0140-6736(20)30628-0 10.1002/jmv.23354 10.1007/s12035-021-02457-z 10.1002/path.1440 10.1002/jmv.25709 10.1056/NEJMoa2015432 10.1080/20009666.2021.1921908 10.1136/bmj-2021-069590 10.1186/s12959-020-00255-6 10.7326/L20-1275 10.1148/ryct.2020200277 10.1148/rg.282075705 10.1155/2022/4640788 10.1097/CM9.0000000000000774 10.1007/s11684-020-0754-0 10.1159/000342483 10.1038/s41467-021-22781-1 10.1016/j.kint.2020.04.003 10.1681/ASN.2020040419 10.2139/ssrn.3559601 10.1016/j.tcm.2020.10.005 10.1016/j.avsg.2020.07.013 10.1016/j.acra.2020.07.019 10.1186/s13054-020-02931-5 10.1007/s00330-020-07300-y 10.1016/j.ajem.2020.07.054 10.1093/eurheartj/ehab314 10.1016/j.ijcard.2020.04.028 10.1128/MMBR.05015-11 10.1016/j.tox.2022.153104 10.1080/15384101.2019.1662678 10.1016/j.ebiom.2020.102763 10.1002/jmv.25900 10.1016/j.ebiom.2020.102789 10.1038/s41584-020-0474-5 10.37899/journallamedihealtico.v3i3.647 10.1016/j.juro.2015.03.119 10.1016/j.idcr.2020.e00968 10.1093/ckj/sfaa141 10.1007/s00134-020-06153-9 10.1016/j.biopha.2021.111966 10.4103/iju.IJU_76_21 10.1016/j.xkme.2020.07.010 10.1097/SHK.0000000000001659 10.1016/j.clinimag.2020.11.011 10.1148/radiol.2020201623 10.4269/ajtmh.20-0869 10.1016/S0140-6736(20)30566-3 10.1016/j.jacc.2020.04.031 10.1016/j.immuni.2020.06.017 10.1016/S0304-4157(98)00018-5 10.1038/nri3345 10.2174/138920111798281171 10.12688/f1000research.9692.1 10.1111/j.1365-2362.2009.02153.x 10.1046/j.1523-1755.62.s82.4.x 10.3390/molecules27072048 10.1161/01.RES.84.9.1043 10.1111/ijlh.13829 10.1161/01.ATV.20.8.2019 10.1016/j.jacc.2020.06.080 10.1002/ccd.29056 10.1111/jocs.14538 10.1016/j.clinimag.2021.02.016 10.1007/s00259-021-05375-3 10.1186/s13244-021-00973-z 10.3174/ajnr.A6674 10.1161/CIRCULATIONAHA.120.047525 10.1016/j.wneu.2020.08.154 10.1016/j.neurad.2020.04.003 10.3389/fcvm.2021.671669 10.1016/j.clinimag.2020.07.007 10.1111/jon.12803 10.1155/2020/7397480 10.1016/j.jvs.2021.11.064 10.1016/j.cmpb.2017.12.016 10.1007/s10916-015-0214-6 10.7785/tcrt.2012.500381 10.1038/nature14539 10.1016/j.nicl.2022.103065 10.1016/j.cmpb.2021.106332 10.1016/j.ejrad.2020.109041 10.1007/s00330-020-07044-9 10.3348/kjr.2020.0536 10.1109/TMI.2020.2994459 10.1002/jmri.26887 10.1148/ryai.2020200048 10.1148/radiol.2020200905 10.1007/s10096-020-03901-z 10.21037/atm.2020.03.132 10.1109/ACCESS.2020.3005510 10.1016/j.cell.2020.04.045 10.1109/TUFFC.2020.3005512 10.1109/ACCESS.2020.3003810 10.1016/j.compbiomed.2020.103869 10.1016/j.cmpb.2020.105608 10.1080/07391102.2020.1788642 10.1111/nan.12667 10.3390/cancers14040987 10.1016/j.cmpb.2017.09.004 10.1002/mp.14193 10.1016/j.ekir.2017.11.002 10.1038/s41746-019-0104-2 10.1007/s11517-005-0016-y 10.7863/jum.2009.28.11.1561 10.1080/03772063.2019.1604176 10.1093/ajcn/65.4.1000 10.1007/s11883-016-0635-9 10.5853/jos.2017.02922 10.1016/j.cmpb.2013.07.012 10.1016/j.bspc.2013.08.008 10.1109/TMI.2016.2528162 10.1515/itms-2017-0007 10.1016/j.compbiomed.2020.103958 10.1016/j.cmpb.2012.05.008 10.1016/j.compbiomed.2018.05.014 10.1177/1544316718806421 10.1016/j.compbiomed.2020.103847 10.3390/diagnostics11122257 10.23736/S0392-9590.21.04771-4 10.1016/j.compbiomed.2016.11.011 10.1109/JBHI.2016.2631401 10.3934/mbe.2022229 10.1504/IJCSE.2022.120788 10.1016/j.jacr.2017.12.028 10.1016/j.ejrad.2017.01.031 10.21037/atm-20-7676 10.1007/s10554-020-02124-9 10.1109/TIM.2021.3052577 10.3390/jcm11133721 10.1049/el.2020.2102 10.3390/electronics11111800 10.1016/j.compbiomed.2022.105273 10.1109/RBME.2020.2990959 10.1016/j.preteyeres.2021.100965 10.1038/s41467-020-17971-2 10.1056/NEJMc2011400 10.1016/j.jacc.2020.05.001 10.1186/s12882-020-02150-8 10.7759/cureus.9540 10.1007/s13204-021-01868-7 10.1007/s11548-021-02317-0 10.3389/fphys.2022.832457 10.1002/cyto.a.24274 10.1038/s41598-019-54244-5 10.1148/radiol.2021211483 10.1016/j.jcmg.2021.10.013 10.1681/ASN.2020050589 10.1016/j.ijantimicag.2020.105949 10.1016/j.mayocp.2020.03.024 10.1148/radiol.2020203511 10.1186/s13054-020-03179-9 10.1055/a-1775-8633 10.1155/2021/6761364 10.1681/ASN.2020050597 10.1093/ejcts/ezac289 10.36660/abc.20200302 10.1016/j.compbiomed.2021.104721 10.1016/j.ejvs.2009.03.013 10.3348/kjr.2021.0148 10.1007/s40520-021-01985-x 10.1007/s00296-021-05062-4 10.1056/NEJM199412013312202 10.1016/j.ultrasmedbio.2014.12.024 10.1227/01.NEU.0000239895.00373.E4 10.1681/ASN.V92231 10.1165/rcmb.2007-0441OC 10.1097/MCA.0000000000000934 10.3390/biology11020221 10.2214/AJR.11.6955 10.1097/MCA.0000000000000914 10.1097/CCM.0000000000004899 10.1002/14651858.CD003186.pub3 10.3390/diagnostics12010166 10.1016/j.bspc.2016.03.001 10.1016/j.compbiomed.2021.105204 10.1109/TIM.2022.3174270 10.1016/S0140-6736(96)07492-2 10.1016/j.compbiomed.2022.105571 10.3390/app10238623 10.1016/j.clim.2020.108509 10.1016/j.jpeds.2020.07.039 10.1007/BF01907940 10.1200/GO.20.00064 10.1016/j.irbm.2020.05.003 10.2214/AJR.20.23034 10.1007/s11604-021-01120-w 10.1148/radiol.2020200343 10.1016/j.asoc.2020.106912 10.1109/TIP.2021.3058783 10.1093/eurheartj/ehaa399

Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.

When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

European journal of radiology open

10.1016/j.ejro.2022.100438

A dual-stage deep convolutional neural network for automatic diagnosis of COVID-19 and pneumonia from chest CT images.

In the Coronavirus disease-2019 (COVID-19) pandemic, for fast and accurate diagnosis of a large number of patients, besides traditional methods, automated diagnostic tools are now extremely required. In this paper, a deep convolutional neural network (CNN) based scheme is proposed for automated accurate diagnosis of COVID-19 from lung computed tomography (CT) scan images. First, for the automated segmentation of lung regions in a chest CT scan, a modified CNN architecture, namely SKICU-Net is proposed by incorporating additional skip interconnections in the U-Net model that overcome the loss of information in dimension scaling. Next, an agglomerative hierarchical clustering is deployed to eliminate the CT slices without significant information. Finally, for effective feature extraction and diagnosis of COVID-19 and pneumonia from the segmented lung slices, a modified DenseNet architecture, namely P-DenseCOVNet is designed where parallel convolutional paths are introduced on top of the conventional DenseNet model for getting better performance through overcoming the loss of positional arguments. Outstanding performances have been achieved with an F

Computers in biology and medicine

10.1016/j.compbiomed.2022.105806 10.1101/2020.02.14.20023028

Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation.

Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, "COVLIAS 1.0-Unseen" proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10,000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations-two Unseen sets: (i) train-CRO:test-ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web-based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL.

Journal of medical systems

10.1007/s10916-022-01850-y 10.23750/abm.v91i1.9397 10.26355/eurrev_202012_24058 10.1016/j.compbiomed.2020.103960 10.1007/s10554-020-02089-9 10.4239/wjd.v12.i3.215 10.26355/eurrev_202108_26464 10.1016/j.clinimag.2021.05.016 10.23750/abm.v92i5.10418 10.52586/5026 10.1148/radiol.2020200432 10.1016/j.ejrad.2020.109041 10.1007/s00330-020-06920-8 10.21037/atm-2020-cass-13 10.1016/j.neurad.2017.09.007 10.21037/atm-20-7676 10.5152/dir.2020.20304 10.1007/s00330-020-06915-5 10.1007/s13244-010-0060-5 10.1080/07853890.2020.1851044 10.2214/AJR.20.23034 10.1148/radiol.2020200343 10.1007/s11548-020-02286-w 10.1007/s11548-021-02317-0 10.1007/s10916-021-01707-w 10.3390/diagnostics12010166 10.1109/JBHI.2021.3103839 10.3390/diagnostics11122257 10.1016/j.wneu.2016.05.069 10.1016/j.ejmp.2017.11.036 10.1016/j.compbiomed.2021.104721 10.1016/j.compbiomed.2021.104803 10.23736/S0392-9590.21.04771-4 10.1016/j.compbiomed.2021.105131 10.1109/TMI.2020.3002417 10.11613/BM.2015.015 10.1080/10408340500526766 10.1177/875647939000600106 10.2741/4725 10.1016/j.ejrad.2019.02.038 10.1007/s10916-018-0940-7 10.1016/j.cmpb.2017.09.004 10.1016/j.cmpb.2019.04.008 10.1007/s10916-010-9645-2 10.1016/j.cmpb.2012.09.008 10.1007/s11517-012-1019-0 10.1177/0954411913483637 10.1016/j.cmpb.2017.12.016 10.1016/j.compbiomed.2017.10.019 10.7785/tcrt.2012.500346 10.1016/j.compbiomed.2015.07.021 10.1016/j.cmpb.2017.07.011 10.1007/s11517-021-02322-0 10.1007/s00330-020-06829-2 10.1109/TNNLS.2021.3054746 10.1109/TMI.2019.2959609 10.1186/s12880-020-00529-5 10.1016/j.acra.2020.09.004 10.1109/TPAMI.2016.2644615 10.1006/jmps.1999.1279 10.1007/978-0-387-39940-9_565 10.1109/TIM.2011.2174897 10.1007/s10916-015-0214-6 10.1016/j.cmpb.2016.02.004 10.1007/s10916-017-0797-1 10.1016/j.eswa.2015.03.014 10.1055/s-0032-1330336 10.1016/j.bspc.2016.03.001 10.1109/4233.992158 10.31083/j.rcm.2020.04.236

Psoas muscle metastatic disease mimicking a psoas abscess on imaging.

Here, we report a case of malignant psoas syndrome presented to us during the second peak of the COVID-19 pandemic. Our patient had a medical history of hypertension, recently diagnosed with left iliac deep vein thrombosis and previous breast and endometrial cancers. She presented with exquisite pain and a fixed flexion deformity of the left hip. A rim-enhancing lesion was seen within the left psoas muscle and was initially deemed to be a psoas abscess. This failed to respond to medical management and attempts at drainage. Subsequent further imaging revealed the mass was of a malignant nature; histology revealing a probable carcinomatous origin. Following diagnosis, palliative input was obtained and, unfortunately, our patient passed away in a hospice shortly after discharge. We discuss the aetiology, radiological findings and potential treatments of this condition and learning points to prompt clinicians to consider this diagnosis in those with a personal history of cancer.

BMJ case reports

10.1136/bcr-2022-250654 10.1186/1470-7330-14-21 10.1007/s00330-009-1577-1 10.1007/s12094-011-0625-x 10.3892/mco.2018.1635 10.1016/j.jpainsymman.2003.12.018 10.1089/jpm.2014.0387 10.4103/IJPC.IJPC_205_19 10.1080/15360288.2017.1301617 10.1016/S0304-3959(99)00039-1 10.1111/papr.12643 10.1159/000360581 10.2214/ajr.174.2.1740401 10.1111/j.1440-1673.1990.tb02831.x 10.11604/pamj.2020.36.231.21137 10.1016/j.gore.2021.100814 10.1016/j.spinee.2015.08.001 10.1136/bcr-2017-223916 10.1102/1470-7330.2013.0011 10.1159/000227588 10.1016/j.ygyno.2006.02.011 10.3233/BD-140384 10.1007/s002560050141

Rapid tissue prototyping with micro-organospheres.

In vitro tissue models hold great promise for modeling diseases and drug responses. Here, we used emulsion microfluidics to form micro-organospheres (MOSs), which are droplet-encapsulated miniature three-dimensional (3D) tissue models that can be established rapidly from patient tissues or cells. MOSs retain key biological features and responses to chemo-, targeted, and radiation therapies compared with organoids. The small size and large surface-to-volume ratio of MOSs enable various applications including quantitative assessment of nutrient dependence, pathogen-host interaction for anti-viral drug screening, and a rapid potency assay for chimeric antigen receptor (CAR)-T therapy. An automated MOS imaging pipeline combined with machine learning overcomes plating variation, distinguishes tumorspheres from stroma, differentiates cytostatic versus cytotoxic drug effects, and captures resistant clones and heterogeneity in drug response. This pipeline is capable of robust assessments of drug response at individual-tumorsphere resolution and provides a rapid and high-throughput therapeutic profiling platform for precision medicine.

Stem cell reports

10.1016/j.stemcr.2022.07.016 10.1016/j.copbio.2015.05.003 10.1038/s41556-020-0472-5 10.1038/s41556-018-0143-y 10.1038/s41556-019-0360-z 10.1016/j.medj.2021.08.005 10.1038/s41551-020-0565-2 10.1038/s41596-019-0232-9 10.1038/nm.3388 10.1016/j.cell.2018.07.009 10.1016/j.stem.2022.04.006 10.1158/2159-8290.CD-18-1522 10.3390/jcm8111880 10.1038/nature14415 10.1002/advs.202102418 10.1038/s41591-019-0584-2 10.1038/ng.3127 10.1038/s41586-020-2575-3 10.1016/j.jtbi.2011.03.026 10.1016/j.xcrm.2020.100161 10.1063/1.4995479 10.1158/1078-0432.CCR-20-5026 10.48550/arXiv.1912.08193 10.1016/j.ccell.2021.12.004 10.1002/advs.201903739 10.1038/nprot.2013.046 10.1158/2326-6066.CIR-18-0428 10.1158/1078-0432.CCR-20-0073 10.1016/j.cell.2018.11.021 10.1126/scitranslmed.aay2574 10.1016/j.cell.2017.11.010 10.15252/embj.2018100300 10.1053/j.gastro.2011.07.050 10.1016/j.stemcr.2021.04.009 10.15252/embj.2018100928 10.1016/j.isci.2020.101372 10.1158/2159-8290.CD-18-0349 10.1016/j.celrep.2020.107670 10.1016/j.cell.2015.03.053 10.1126/science.aao2774 10.1039/d0bm01085e 10.1016/j.stem.2019.10.010

DAFLNet: Dual Asymmetric Feature Learning Network for COVID-19 Disease Diagnosis in X-Rays.

COVID-19 has become the largest public health event worldwide since its outbreak, and early detection is a prerequisite for effective treatment. Chest X-ray images have become an important basis for screening and monitoring the disease, and deep learning has shown great potential for this task. Many studies have proposed deep learning methods for automated diagnosis of COVID-19. Although these methods have achieved excellent performance in terms of detection, most have been evaluated using limited datasets and typically use a single deep learning network to extract features. To this end, the dual asymmetric feature learning network (DAFLNet) is proposed, which is divided into two modules, DAFFM and WDFM. DAFFM mainly comprises the backbone networks EfficientNetV2 and DenseNet for feature fusion. WDFM is mainly for weighted decision-level fusion and features a new pretrained network selection algorithm (PNSA) for determination of the optimal weights. Experiments on a large dataset were conducted using two schemes, DAFLNet-1 and DAFLNet-2, and both schemes outperformed eight state-of-the-art classification techniques in terms of classification performance. DAFLNet-1 achieved an average accuracy of up to 98.56% for the triple classification of COVID-19, pneumonia, and healthy images.

Computational and mathematical methods in medicine

10.1155/2022/3836498 10.1016/j.cmpb.2020.105532 10.1148/radiol.2020200642 10.1016/j.inffus.2020.10.004 10.22266/ijies2016.1231.24 10.3390/diagnostics10060358 10.1016/j.chaos.2020.110190 10.1016/j.crad.2020.03.004 10.1016/j.imu.2020.100360 10.1016/j.compeleceng.2020.106765 10.1007/s12559-020-09776-8 10.1016/j.ins.2020.09.041 10.1016/j.compbiomed.2020.103792 10.1016/j.bspc.2019.04.031 10.1016/j.inffus.2020.11.005 10.1016/j.compbiomed.2020.103805 10.1016/j.bspc.2020.102257 10.1016/j.bspc.2022.103677 10.1007/978-3-030-01234-2_1 10.1186/s13040-021-00244-z 10.1016/j.eswa.2022.116540 10.1007/s11548-020-02283-z 10.2196/19569 10.1007/978-3-319-46493-0_38

MFL-Net: An Efficient Lightweight Multi-Scale Feature Learning CNN for COVID-19 Diagnosis From CT Images.

Timely and accurate diagnosis of coronavirus disease 2019 (COVID-19) is crucial in curbing its spread. Slow testing results of reverse transcription-polymerase chain reaction (RT-PCR) and a shortage of test kits have led to consider chest computed tomography (CT) as an alternative screening and diagnostic tool. Many deep learning methods, especially convolutional neural networks (CNNs), have been developed to detect COVID-19 cases from chest CT scans. Most of these models demand a vast number of parameters which often suffer from overfitting in the presence of limited training data. Moreover, the linearly stacked single-branched architecture based models hamper the extraction of multi-scale features, reducing the detection performance. In this paper, to handle these issues, we propose an extremely lightweight CNN with multi-scale feature learning blocks called as MFL-Net. The MFL-Net comprises a sequence of MFL blocks that combines multiple convolutional layers with 3 ×3 filters and residual connections effectively, thereby extracting multi-scale features at different levels and preserving them throughout the block. The model has only 0.78M parameters and requires low computational cost and memory space compared to many ImageNet pretrained CNN architectures. Comprehensive experiments are carried out using two publicly available COVID-19 CT imaging datasets. The results demonstrate that the proposed model achieves higher performance than pretrained CNN models and state-of-the-art methods on both datasets with limited training data despite having an extremely lightweight architecture. The proposed method proves to be an effective aid for the healthcare system in the accurate and timely diagnosis of COVID-19.

IEEE journal of biomedical and health informatics

10.1109/JBHI.2022.3196489

Semi-supervised COVID-19 CT image segmentation using deep generative models.

A recurring problem in image segmentation is a lack of labelled data. This problem is especially acute in the segmentation of lung computed tomography (CT) of patients with Coronavirus Disease 2019 (COVID-19). The reason for this is simple: the disease has not been prevalent long enough to generate a great number of labels. Semi-supervised learning promises a way to learn from data that is unlabelled and has seen tremendous advancements in recent years. However, due to the complexity of its label space, those advancements cannot be applied to image segmentation. That being said, it is this same complexity that makes it extremely expensive to obtain pixel-level labels, making semi-supervised learning all the more appealing. This study seeks to bridge this gap by proposing a novel model that utilizes the image segmentation abilities of deep convolution networks and the semi-supervised learning abilities of generative models for chest CT images of patients with the COVID-19. We propose a novel generative model called the shared variational autoencoder (SVAE). The SVAE utilizes a five-layer deep hierarchy of latent variables and deep convolutional mappings between them, resulting in a generative model that is well suited for lung CT images. Then, we add a novel component to the final layer of the SVAE which forces the model to reconstruct the input image using a segmentation that must match the ground truth segmentation whenever it is present. We name this final model StitchNet. We compare StitchNet to other image segmentation models on a high-quality dataset of CT images from COVID-19 patients. We show that our model has comparable performance to the other segmentation models. We also explore the potential limitations and advantages in our proposed algorithm and propose some potential future research directions for this challenging issue.

BMC bioinformatics

10.1186/s12859-022-04878-6 10.1016/j.cell.2020.04.045 10.1109/TPAMI.2016.2644615 10.1109/TPAMI.2019.2960224 10.1016/j.patcog.2020.107269 10.1186/s12967-021-02992-2

CovMnet-Deep Learning Model for classifying Coronavirus (COVID-19).

Diagnosing COVID-19, current pandemic disease using Chest X-ray images is widely used to evaluate the lung disorders. As the spread of the disease is enormous many medical camps are being conducted to screen the patients and Chest X-ray is a simple imaging modality to detect presence of lung disorders. Manual lung disorder detection using Chest X-ray by radiologist is a tedious process and may lead to inter and intra-rate errors. Various deep convolution neural network techniques were tested for detecting COVID-19 abnormalities in lungs using Chest X-ray images. This paper proposes deep learning model to classify COVID-19 and normal chest X-ray images. Experiments are carried out for deep feature extraction, fine-tuning of convolutional neural networks (CNN) hyper parameters, and end-to-end training of four variants of the CNN model. The proposed CovMnet provide better classification accuracy of 97.4% for COVID-19 /normal than those reported in the previous studies. The proposed CovMnet model has potential to aid radiologist to monitor COVID-19 disease and proves to be an efficient non-invasive COVID-19 diagnostic tool for lung disorders.

Health and technology

10.1007/s12553-022-00688-1 10.1016/j.chemolab.2020.104054 10.1007/s10044-021-00984-y 10.1007/s13246-020-00865-4 10.1016/j.ins.2020.09.041 10.1016/j.chaos.2020.109949 10.1016/j.chaos.2020.110242 10.4018/IJSSCI.2020070102 10.4249/scholarpedia.1717 10.1113/jphysiol.1970.sp009022 10.1007/BF00344251 10.1007/s00521-018-3761-1 10.1007/s10096-020-03901-z

Multiclass Classification for Detection of COVID-19 Infection in Chest X-Rays Using CNN.

Coronavirus took the world by surprise and caused a lot of trouble in all the important fields in life. The complexity of dealing with coronavirus lies in the fact that it is highly infectious and is a novel virus which is hard to detect with exact precision. The typical detection method for COVID-19 infection is the RT-PCR but it is a rather expensive method which is also invasive and has a high margin of error. Radiographies are a good alternative for COVID-19 detection given the experience of the radiologist and his learning capabilities. To make an accurate detection from chest X-Rays, deep learning technologies can be involved to analyze the radiographs, learn distinctive patterns of coronavirus' presence, find these patterns in the tested radiograph, and determine whether the sample is actually COVID-19 positive or negative. In this study, we propose a model based on deep learning technology using Convolutional Neural Networks and training it on a dataset containing a total of over 35,000 chest X-Ray images, nearly 16,000 for COVID-19 positive images, 15,000 for normal images, and 5,000 for pneumonia-positive images. The model's performance was assessed in terms of accuracy, precision, recall, and

Computational intelligence and neuroscience

10.1155/2022/3289809 10.1016/j.chaos.2020.110495 10.1016/j.bspc.2022.103561 10.1007/s13755-020-00135-3 10.3390/ijerph18063056 10.1007/978-3-642-15825-4_10 10.3390/s2203121 10.1007/s10489-020-01978-9 10.1016/j.neucom.2021.03.034 10.1016/j.bspc.2021.102920

Innovations in thoracic imaging: CT, radiomics, AI and x-ray velocimetry.

In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of 'non visual' markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID-19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x-ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra-low-dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon-counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X-ray velocimetry integrates x-ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation.

Respirology (Carlton, Vic.)

10.1111/resp.14344 10.1097/RLI.0000000000000822 10.1148/radiol.210551 10.1007/s00247-021-05146-0 10.1109/CVPR.2017.369

Reinforcement Learning Based Diagnosis and Prediction for COVID-19 by Optimizing a Mixed Cost Function From CT Images.

A novel coronavirus disease (COVID-19) is a pandemic disease has caused 4 million deaths and more than 200 million infections worldwide (as of August 4, 2021). Rapid and accurate diagnosis of COVID-19 infection is critical to controlling the spread of the epidemic. In order to quickly and efficiently detect COVID-19 and reduce the threat of COVID-19 to human survival, we have firstly proposed a detection framework based on reinforcement learning for COVID-19 diagnosis, which constructs a mixed loss function that can integrate the advantages of multiple loss functions. This paper uses the accuracy of the validation set as the reward value, and obtains the initial model for the next epoch by searching the model corresponding to the maximum reward value in each epoch. We also have proposed a prediction framework that integrates multiple detection frameworks using parameter sharing to predict the progression of patients' disease without additional training. This paper also constructed a higher-quality version of the CT image dataset containing 247 cases screened by professional physicians, and obtained more excellent results on this dataset. Meanwhile, we used the other two COVID-19 datasets as external verifications, and still achieved a high accuracy rate without additional training. Finally, the experimental results show that our classification accuracy can reach 98.31%, and the precision, sensitivity, specificity, and AUC (Area Under Curve) are 98.82%, 97.99%, 98.67%, and 0.989, respectively. The accuracy of external verification can reach 93.34% and 91.05%. What's more, the accuracy of our prediction framework is 91.54%. A large number of experiments demonstrate that our proposed method is effective and robust for COVID-19 detection and prediction.

IEEE journal of biomedical and health informatics

10.1109/JBHI.2022.3197666

Detection of COVID-19 from chest X-ray images: Boosting the performance with convolutional neural network and transfer learning.

Coronavirus disease (COVID-19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID-19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID-19 can protect people from severe infection. Unfortunately, COVID-19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID-19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID-19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID-19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID-19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre-trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X-rays (1200 cases of COVID-19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X-ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID-19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of

Expert systems

10.1111/exsy.13099 10.1080/07391102.2020.1767212 10.20944/preprints202003.0300.v1

A modified DeepLabV3+ based semantic segmentation of chest computed tomography images for COVID-19 lung infections.

Coronavirus disease (COVID-19) affects the lives of billions of people worldwide and has destructive impacts on daily life routines, the global economy, and public health. Early diagnosis and quantification of COVID-19 infection have a vital role in improving treatment outcomes and interrupting transmission. For this purpose, advances in medical imaging techniques like computed tomography (CT) scans offer great potential as an alternative to RT-PCR assay. CT scans enable a better understanding of infection morphology and tracking of lesion boundaries. Since manual analysis of CT can be extremely tedious and time-consuming, robust automated image segmentation is necessary for clinical diagnosis and decision support. This paper proposes an efficient segmentation framework based on the modified DeepLabV3+ using lower atrous rates in the Atrous Spatial Pyramid Pooling (ASPP) module. The lower atrous rates make receptive small to capture intricate morphological details. The encoder part of the framework utilizes a pre-trained residual network based on dilated convolutions for optimum resolution of feature maps. In order to evaluate the robustness of the modified model, a comprehensive comparison with other state-of-the-art segmentation methods was also performed. The experiments were carried out using a fivefold cross-validation technique on a publicly available database containing 100 single-slice CT scans from >40 patients with COVID-19. The modified DeepLabV3+ achieved good segmentation performance using around 43.9 M parameters. The lower atrous rates in the ASPP module improved segmentation performance. After fivefold cross-validation, the framework achieved an overall Dice similarity coefficient score of 0.881. The results demonstrate that several minor modifications to the DeepLabV3+ pipeline can provide robust solutions for improving segmentation performance and hardware implementation.

International journal of imaging systems and technology

10.1002/ima.22772 10.1002/ima.22566 10.1002/ima.22525 10.1016/j.measurement.2020.108288 10.1016/j.mehy.2020.109761 10.1148/radiol.2020200642 10.1016/j.aej.2020.10.046 10.1016/j.media.2017.07.005 10.1016/j.tmaid.2020.101623 10.1016/j.jrid.2020.04.001 10.1111/exsy.12742 10.1049/iet-cvi.2018.5129 10.1049/iet-its.2018.5144 10.1016/j.specom.2017.02.009 10.1016/j.eswa.2021.115465 10.1016/j.compbiomed.2020.104037 10.1016/j.clinimag.2021.01.019 10.1109/ICDMW.2018.00176 10.30897/ijegeo.737993 10.1016/j.media.2020.101794 10.1186/s12880-020-00529-5 10.1016/j.imu.2021.100681 10.1109/CVPR.2016.90 10.1007/s11042-020-09634-7 10.1109/ACCESS.2016.2624938 10.1016/j.compbiomed.2021.105134 10.1016/j.compbiomed.2022.105383 10.1007/s10278-021-00434-5 10.1007/978-3-319-24574-4_28 10.1109/TPAMI.2016.2572683 10.1155/2021/9999368 10.1145/3453892.3461322 10.1016/j.asoc.2020.106897 10.1016/j.cmpb.2021.106004 10.1016/j.patcog.2022.108538 10.31590/ejosat.819409 10.3390/s20113183 10.1016/j.patrec.2020.07.029 10.1109/WACV.2018.00163 10.3390/su13031224 10.3390/diagnostics11091712 10.1007/978-3-030-01234-2_49 10.1007/978-3-319-10578-9_23 10.1016/j.eswa.2020.114417 10.1002/mp.14676 10.48550/arXiv.1412.6980 10.1007/s10462-020-09854-1 10.5281/ZENODO.3757476 10.1109/ACCESS.2021.3067047

Deep Learning-Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs.

Accurate estimation of mortality and time to death at admission for COVID-19 patients is important and several deep learning models have been created for this task. However, there are currently no prognostic models which use end-to-end deep learning to predict time to event for admitted COVID-19 patients using chest radiographs and clinical data. We retrospectively implemented a new artificial intelligence model combining DeepSurv (a multiple-perceptron implementation of the Cox proportional hazards model) and a convolutional neural network (CNN) using 1356 COVID-19 inpatients. For comparison, we also prepared DeepSurv only with clinical data, DeepSurv only with images (CNNSurv), and Cox proportional hazards models. Clinical data and chest radiographs at admission were used to estimate patient outcome (death or discharge) and duration to the outcome. The Harrel's concordance index (c-index) of the DeepSurv with CNN model was 0.82 (0.75-0.88) and this was significantly higher than the DeepSurv only with clinical data model (c-index = 0.77 (0.69-0.84), p = 0.011), CNNSurv (c-index = 0.70 (0.63-0.79), p = 0.001), and the Cox proportional hazards model (c-index = 0.71 (0.63-0.79), p = 0.001). These results suggest that the time-to-event prognosis model became more accurate when chest radiographs and clinical data were used together.

Journal of digital imaging

10.1007/s10278-022-00691-y 10.7861/clinmed.2020-0214 10.1186/s13613-020-00650-2 10.1093/cid/ciaa414 10.1016/S2213-8587(21)00089-9 10.1001/jama.2018.11100 10.1038/nature14539 10.1186/s12874-018-0482-1 10.1016/j.amjmed.2004.03.020 10.1007/s11547-020-01232-9 10.1007/s10140-020-01808-y 10.1148/radiol.2020201754 10.1148/radiol.2020200823 10.1007/s00330-020-06827-4 10.1007/s10278-013-9622-7 10.1136/bmj.h5527 10.1136/bmj.n2400 10.1001/jamainternmed.2021.6203 10.1056/NEJMoa2103417 10.1016/j.jiph.2021.09.023 10.1371/journal.pone.0241955 10.1038/s41586-020-2521-4 10.1023/A:1010933404324 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2 10.1111/j.0006-341X.2005.030814.x 10.1136/bmj.m1328 10.2196/25535 10.2196/24973 10.1016/j.media.2021.102096 10.1148/ryai.2020200098 10.1038/s41598-022-07890-1 10.1038/s41598-021-93543-8 10.1038/s41598-019-43372-7 10.1016/S2589-7500(21)00039-X 10.1016/j.lfs.2020.117788 10.1186/s13054-019-2663-7 10.1001/jamanetworkopen.2020.25881 10.1001/jamanetworkopen.2020.5842 10.1002/acm2.12995 10.1007/BF00344251 10.1148/radiol.2017171183 10.1056/NEJMc2104626

Deep Learning-Aided Automated Pneumonia Detection and Classification Using CXR Scans.

The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled almost all countries. The pandemic has mounted pressure on the existing healthcare system and caused panic and desperation. The gold testing standard for COVID-19 detection, reverse transcription-polymerase chain reaction (RT-PCR), has shown its limitations with 70% accuracy, contributing to the incorrect diagnosis that exaggerated the complexities and increased the fatalities. The new variations further pose unseen challenges in terms of their diagnosis and subsequent treatment. The COVID-19 virus heavily impacts the lungs and fills the air sacs with fluid causing pneumonia. Thus, chest X-ray inspection is a viable option if the inspection detects COVID-19-induced pneumonia, hence confirming the exposure of COVID-19. Artificial intelligence and machine learning techniques are capable of examining chest X-rays in order to detect patterns that can confirm the presence of COVID-19-induced pneumonia. This research used CNN and deep learning techniques to detect COVID-19-induced pneumonia from chest X-rays. Transfer learning with fine-tuning ensures that the proposed work successfully classifies COVID-19-induced pneumonia, regular pneumonia, and normal conditions. Xception, Visual Geometry Group 16, and Visual Geometry Group 19 are used to realize transfer learning. The experimental results were promising in terms of precision, recall, F1 score, specificity, false omission rate, false negative rate, false positive rate, and false discovery rate with a COVID-19-induced pneumonia detection accuracy of 98%. Experimental results also revealed that the proposed work has not only correctly identified COVID-19 exposure but also made a distinction between COVID-19-induced pneumonia and regular pneumonia, as the latter is a very common disease, while COVID-19 is more lethal. These results mitigated the concern and overlap in the diagnosis of COVID-19-induced pneumonia and regular pneumonia. With further integrations, it can be employed as a potential standard model in differentiating the various lung-related infections, including COVID-19.

Computational intelligence and neuroscience

10.1155/2022/7474304 10.1109/ICDABI51230.2020.9325626 10.1001/jama.2020.1585 10.1016/s0140-6736(20)30211-710.1016/s0140-6736(20)30211-7 10.1056/NEJMoa2001316 10.1016/S0140-6736(20)30183-5 10.1093/clinchem/hvaa029 10.1148/radiol.2020200230 10.2214/AJR.20.23034 10.1007/s10489-020-01826-w 10.1109/ISMSIT.2019.8932878 10.1109/NSSMIC.2018.8824292 10.1109/ICNSC.2018.8361312 10.1007/s11517-019-01965-4 10.1109/TMI.2019.2894349 10.1148/radiol.2019181960 10.3390/app10020559 10.1016/j.compbiomed.2020.103869 10.1145/3195588.3195597 10.1148/radiol.2020200905 10.1371/journal.pmed.1002686 10.1109/ACCESS.2020.3010287 10.3390/app9194130 10.1109/TMI.2020.2994459 10.17632/9xkhgts2s6.1 10.1007/s40009-020-00979-z 10.1007/s11042-022-12775-6

A Novel Multi-Stage Residual Feature Fusion Network for Detection of COVID-19 in Chest X-Ray Images.

To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial, chest screening with radiography imaging plays an important role in addition to the real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test. Due to the limited data, existing models suffer from incapable feature extraction and poor network convergence and optimization. Accordingly, a multi-stage residual network, MSRCovXNet, is proposed for effective detection of COVID-19 from chest x-ray (CXR) images. As a shallow yet effective classifier with the ResNet-18 as the feature extractor, MSRCovXNet is optimized by fusing two proposed feature enhancement modules (FEM), i.e., low-level and high-level feature maps (LLFMs and HLFMs), which contain respectively more local information and rich semantic information, respectively. For effective fusion of these two features, a single-stage FEM (MSFEM) and a multi-stage FEM (MSFEM) are proposed to enhance the semantic feature representation of the LLFMs and the local feature representation of the HLFMs, respectively. Without ensembling other deep learning models, our MSRCovXNet has a precision of 98.9% and a recall of 94% in detection of COVID-19, which outperforms several state-of-the-art models. When evaluated on the COVIDGR dataset, an average accuracy of 82.2% is achieved, leading other methods by at least 1.2%.

IEEE transactions on molecular, biological, and multi-scale communications

10.1109/TMBMC.2021.3099367

Deep Learning Based COVID-19 Detection Using Medical Images: Is Insufficient Data Handled Well?

Deep learning is a prominent method for automatic detection of COVID-19 disease using a medical dataset. This paper aims to give a perspective on the data insufficiency issue that exists in COVID-19 detection associated with deep learning. The extensive study of the available datasets comprising CT and X-ray images is presented in this paper, which can be very much useful in the context of a deep learning framework for COVID-19 detection. Moreover, various data handling techniques that are very essential in deep learning models are discussed in detail. Advanced data handling techniques and approaches to modify deep learning models are suggested to handle the data insufficiency problem in deep learning based on COVID-19 detection.

Current medical imaging

10.2174/1573405618666220803123626

Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays.

X-ray images aid medical professionals in the diagnosis and detection of pathologies. They are critical, for example, in the diagnosis of pneumonia, the detection of masses, and, more recently, the detection of COVID-19-related conditions. The chest X-ray is one of the first imaging tests performed when pathology is suspected because it is one of the most accessible radiological examinations. Deep learning-based neural networks, particularly convolutional neural networks, have exploded in popularity in recent years and have become indispensable tools for image classification. Transfer learning approaches, in particular, have enabled the use of previously trained networks' knowledge, eliminating the need for large data sets and lowering the high computational costs associated with this type of network. This research focuses on using deep learning-based neural networks to detect anomalies in chest X-rays. Different convolutional network-based approaches are investigated using the ChestX-ray14 database, which contains over 100,000 X-ray images with labels relating to 14 different pathologies, and different classification objectives are evaluated. Starting with the pretrained networks VGG19, ResNet50, and Inceptionv3, networks based on transfer learning are implemented, with different schemes for the classification stage and data augmentation. Similarly, an ad hoc architecture is proposed and evaluated without transfer learning for the classification objective with more examples. The results show that transfer learning produces acceptable results in most of the tested cases, indicating that it is a viable first step for using deep networks when there are not enough labeled images, which is a common problem when working with medical images. The ad hoc network, on the other hand, demonstrated good generalization with data augmentation and an acceptable accuracy value. The findings suggest that using convolutional neural networks with and without transfer learning to design classifiers for detecting pathologies in chest X-rays is a good idea.

BioMed research international

10.1155/2022/7833516 10.1201/b10866-37 10.1109/CVPR.2017.369 10.1109/ICSCCC.2018.8703316 10.1016/B978-0-12-816718-2.00008-7 10.1155/2022/1959371 10.1007/978-981-15-4112-4_7 10.3390/jcm11072054 10.23919/MIPRO48935.2020.9245376 10.1155/2022/4569879 10.14569/IJACSA.2021.0121026 10.1109/ELNANO.2018.8477564 10.1155/2021/8148772 10.1155/2022/3294954 10.1007/s00607-021-00992-0 10.1155/2021/5759184 10.1155/2021/6799202 10.24191/mjoc.v4i1.6095 10.1007/s11548-020-02305-w 10.1155/2021/1220374 10.1117/12.2293971 10.1007/s10916-021-01745-4

Detecting COVID-19 patients via MLES-Net deep learning models from X-Ray images.

Corona Virus Disease 2019 (COVID-19) first appeared in December 2019, and spread rapidly around the world. COVID-19 is a pneumonia caused by novel coronavirus infection in 2019. COVID-19 is highly infectious and transmissible. By 7 May 2021, the total number of cumulative number of deaths is 3,259,033. In order to diagnose the infected person in time to prevent the spread of the virus, the diagnosis method for COVID-19 is extremely important. To solve the above problems, this paper introduces a Multi-Level Enhanced Sensation module (MLES), and proposes a new convolutional neural network model, MLES-Net, based on this module. Attention has the ability to automatically focus on the key points in various information, and Attention can realize parallelism, which can replace some recurrent neural networks to a certain extent and improve the efficiency of the model. We used the correlation between global and local features to generate the attention mask. First, the feature map was divided into multiple groups, and the initial attention mask was obtained by the dot product of each feature group and the feature after the global pooling. Then the attention masks were normalized. At the same time, there were two scaling and translating parameters in each group so that the normalize operation could be restored. Then, the final attention mask was obtained through the sigmoid function, and the feature of each location in the original feature group was scaled. Meanwhile, we use different classifiers on the network models with different network layers. The network uses three classifiers, FC module (fully connected layer), GAP module (global average pooling layer) and GAPFC module (global average pooling layer and fully connected layer), to improve recognition efficiency. GAPFC as a classifier can obtain the best comprehensive effect by comparing the number of parameters, the amount of calculation and the detection accuracy. The experimental results show that the MLES-Net56-GAPFC achieves the best overall accuracy rate (95.27%) and the best recognition rate for COVID-19 category (100%). MLES-Net56-GAPFC has good classification ability for the characteristics of high similarity between categories of COVID-19 X-Ray images and low intra-category variability. Considering the factors such as accuracy rate, number of network model parameters and calculation amount, we believe that the MLES-Net56-GAPFC network model has better practicability.

BMC medical imaging

10.1186/s12880-022-00861-y 10.1016/j.physio.2020.03.003 10.1109/5.726791 10.1109/TIP.2017.2710620 10.2991/ijcis.d.191209.001 10.1186/s12880-019-0399-0 10.1109/TUFFC.2020.3005512 10.1109/ACCESS.2020.3001973 10.7150/ijms.46684 10.1109/TMI.2020.2995508 10.1007/s42979-020-00401-x 10.1007/s42979-020-00335-4 10.1007/s42979-020-00300-1 10.1109/ACCESS.2021.3058537 10.1007/s42979-020-00216-w 10.1007/s42979-020-00383-w 10.2991/ijcis.d.201123.001 10.1016/j.compbiomed.2020.103792 10.1016/j.compbiomed.2020.103869 10.1109/ACCESS.2020.3003810 10.1371/journal.pone.0235187 10.1016/j.imu.2020.100412 10.1049/ipr2.12474 10.1016/j.imu.2020.100505

A comparison of Covid-19 early detection between convolutional neural networks and radiologists.

The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.

Insights into imaging

10.1186/s13244-022-01250-3 10.1001/JAMA.2020.21694 10.1007/S00330-020-07347-X 10.1007/S00330-020-06967-7 10.1148/RADIOL.2020201160/ASSET/IMAGES/LARGE/RADIOL.2020201160.FIG6.JPEG 10.1148/RADIOL.2020202944/ASSET/IMAGES/LARGE/RADIOL.2020202944.TBL4.JPEG 10.1148/RADIOL.2020203511/ASSET/IMAGES/LARGE/RADIOL.2020203511.FIG6C.JPEG 10.1148/RADIOL.2021204522/ASSET/IMAGES/LARGE/RADIOL.2021204522.FIG8C.JPEG 10.1109/TMI.2020.2993291 10.1007/s00330-020-07354-y 10.1007/s00330-020-07270-1 10.1148/RADIOL.2020201874 10.1016/J.MAYOCP.2020.07.024 10.1109/TKDE.2009.191 10.1037/H0031619 10.1214/ss/1177013815 10.1002/1097-0142 10.1148/RADIOL.2020201365/ASSET/IMAGES/LARGE/RADIOL.2020201365.TBL2.JPEG 10.1016/J.JACR.2019.05.019 10.1186/S41747-020-00203-Z/FIGURES/3 10.1148/RADIOL.2020204226

Automatic scoring of COVID-19 severity in X-ray imaging based on a novel deep learning workflow.

In this study, we propose a two-stage workflow used for the segmentation and scoring of lung diseases. The workflow inherits quantification, qualification, and visual assessment of lung diseases on X-ray images estimated by radiologists and clinicians. It requires the fulfillment of two core stages devoted to lung and disease segmentation as well as an additional post-processing stage devoted to scoring. The latter integrated block is utilized, mainly, for the estimation of segment scores and computes the overall severity score of a patient. The models of the proposed workflow were trained and tested on four publicly available X-ray datasets of COVID-19 patients and two X-ray datasets of patients with no pulmonary pathology. Based on a combined dataset consisting of 580 COVID-19 patients and 784 patients with no disorders, our best-performing algorithm is based on a combination of DeepLabV3 + , for lung segmentation, and MA-Net, for disease segmentation. The proposed algorithms' mean absolute error (MAE) of 0.30 is significantly reduced in comparison to established COVID-19 algorithms; BS-net and COVID-Net-S, possessing MAEs of 2.52 and 1.83 respectively. Moreover, the proposed two-stage workflow was not only more accurate but also computationally efficient, it was approximately 11 times faster than the mentioned methods. In summary, we proposed an accurate, time-efficient, and versatile approach for segmentation and scoring of lung diseases illustrated for COVID-19 and with broader future applications for pneumonia, tuberculosis, pneumothorax, amongst others.

Scientific reports

10.1038/s41598-022-15013-z 10.2139/ssrn.3685938 10.1093/cid/ciaa1012 10.1016/j.jaci.2020.04.006 10.1016/j.cmi.2020.04.012 10.1148/ryct.2020200034 10.1148/radiol.2020200527 10.1016/j.chest.2020.04.003 10.1007/s11547-020-01202-1 10.1007/s10489-020-01829-7 10.1016/j.imu.2021.100835 10.1016/j.compbiomed.2020.103869 10.1007/s13755-020-00116-6 10.1016/j.eswa.2020.114054 10.1007/s42600-020-00091-7 10.1109/ACCESS.2020.3025372 10.17632/8gf9vpkhgy.1 10.17632/36fjrg9s69.1 10.1016/j.media.2021.102046 10.1038/s41598-021-88538-4 10.1177/0885066603251897 10.1371/journal.pone.0093885 10.1186/s12931-019-1201-0 10.1038/s41572-018-0051-2 10.1371/journal.pone.0197418 10.1186/s12880-015-0103-y 10.1136/thoraxjnl-2017-211280 10.3389/fphys.2021.672823 10.1186/s12890-020-01286-5 10.1148/radiol.2020201160 10.1038/s41598-020-79470-0 10.1016/j.ijid.2020.05.021 10.1007/s00330-020-07270-1 10.11613/BM.2012.031 10.1109/TIP.2010.2044963 10.1016/j.ijmedinf.2014.10.004 10.1016/j.afjem.2020.09.009

Mortality Prediction Analysis among COVID-19 Inpatients Using Clinical Variables and Deep Learning Chest Radiography Imaging Features.

The emergence of the COVID-19 pandemic over a relatively brief interval illustrates the need for rapid data-driven approaches to facilitate clinical decision making. We examined a machine learning process to predict inpatient mortality among COVID-19 patients using clinical and chest radiographic data. Modeling was performed with a de-identified dataset of encounters prior to widespread vaccine availability. Non-imaging predictors included demographics, pre-admission clinical history, and past medical history variables. Imaging features were extracted from chest radiographs by applying a deep convolutional neural network with transfer learning. A multi-layer perceptron combining 64 deep learning features from chest radiographs with 98 patient clinical features was trained to predict mortality. The Local Interpretable Model-Agnostic Explanations (LIME) method was used to explain model predictions. Non-imaging data alone predicted mortality with an ROC-AUC of 0.87 ± 0.03 (mean ± SD), while the addition of imaging data improved prediction slightly (ROC-AUC: 0.91 ± 0.02). The application of LIME to the combined imaging and clinical model found HbA1c values to contribute the most to model prediction (17.1 ± 1.7%), while imaging contributed 8.8 ± 2.8%. Age, gender, and BMI contributed 8.7%, 8.2%, and 7.1%, respectively. Our findings demonstrate a viable explainable AI approach to quantify the contributions of imaging and clinical data to COVID mortality predictions.

Tomography (Ann Arbor, Mich.)

10.3390/tomography8040151 10.1038/s41586-020-2008-3 10.1148/radiol.2020200490 10.1002/path.5549 10.1148/radiol.2020200642 10.1109/RBME.2020.2987975 10.1016/j.bbe.2020.08.008 10.1038/s41598-020-76550-z 10.1038/s41591-020-0931-3 10.1109/TMI.2020.2993291 10.1148/radiol.2020204226 10.1186/s40537-016-0043-6 10.1007/s10278-013-9622-7 10.1007/978-3-319-24574-4_28 10.2214/ajr.174.1.1740071 10.1016/j.media.2005.02.002 10.1371/journal.pone.0190069 10.1109/ACCESS.2021.3086020 10.1109/ACCESS.2020.2976199 10.1016/S2589-7500(21)00039-X 10.1007/s00330-022-08588-8 10.1183/13993003.02113-2020 10.7717/peerj.10337 10.1186/s12911-021-01742-0 10.7717/peerj-cs.889 10.3389/fdgth.2021.681608 10.3390/diagnostics11081383 10.1002/dmrr.3476

Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images.

(1) Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Reverse transcription polymerase chain reaction (RT-PCR) remains the current gold standard for detecting SARS-CoV-2 infections in nasopharyngeal swabs. In Romania, the first reported patient to have contracted COVID-19 was officially declared on 26 February 2020. (2) Methods: This study proposes a federated learning approach with pre-trained deep learning models for COVID-19 detection. Three clients were locally deployed with their own dataset. The goal of the clients was to collaborate in order to obtain a global model without sharing samples from the dataset. The algorithm we developed was connected to our internal picture archiving and communication system and, after running backwards, it encountered chest CT changes suggestive for COVID-19 in a patient investigated in our medical imaging department on the 28 January 2020. (4) Conclusions: Based on our results, we recommend using an automated AI-assisted software in order to detect COVID-19 based on the lung imaging changes as an adjuvant diagnostic method to the current gold standard (RT-PCR) in order to greatly enhance the management of these patients and also limit the spread of the disease, not only to the general population but also to healthcare professionals.

Life (Basel, Switzerland)

10.3390/life12070958 10.3389/fmicb.2020.631736 10.1002/jmv.25766 10.1056/NEJMoa2001017 10.3390/life12010077 10.1148/ryct.2020200034 10.1016/j.jmoldx.2021.04.009 10.47162/RJME.61.2.21 10.1007/s00330-021-07937-3 10.1111/exsy.12759 10.1038/nature14539 10.1016/j.patcog.2021.108081 10.1016/j.asoc.2020.106912 10.1007/s13246-020-00865-4 10.1016/j.compbiomed.2020.103869 10.3390/diagnostics10060358 10.1016/j.imu.2020.100360 10.1109/ACCESS.2020.3010287 10.1007/s00264-020-04609-7 10.1016/j.cmpb.2020.105608 10.1016/j.cmpb.2020.105581 10.1016/j.eswa.2020.114054 10.1016/j.compbiomed.2020.103795 10.1007/s10489-020-01826-w 10.2196/19569 10.1183/13993003.00775-2020 10.1016/j.asoc.2021.107330 10.1109/JSEN.2021.3076767 10.7910/DVN/6ACUZJ 10.17632/3y55vgckg6.2 10.1148/radiol.11092149 10.12968/hmed.2020.0077 10.5114/pjr.2021.103237 10.1016/j.ijid.2014.12.007 10.1016/j.crad.2016.06.110 10.1148/radiographics.21.2.g01mr17403 10.17632/ygvgkdbmvt.1 10.7937/TCIA.2020.NNC2-0461 10.1073/pnas.79.8.2554 10.1109/EMBC.2017.8037515 10.1007/s10278-021-00508-4 10.21037/jtd.2017.03.157 10.1167/tvst.9.2.14 10.1364/AO.29.004790 10.1016/j.jbi.2014.05.006 10.1016/j.jacr.2022.03.015 10.1109/TCOMM.2020.2990686 10.11919/j.issn.1002-0829.215010 10.1007/s11263-019-01228-7

Bag of Tricks for Improving Deep Learning Performance on Multimodal Image Classification.

A comprehensive medical image-based diagnosis is usually performed across various image modalities before passing a final decision; hence, designing a deep learning model that can use any medical image modality to diagnose a particular disease is of great interest. The available methods are multi-staged, with many computational bottlenecks in between. This paper presents an improved end-to-end method of multimodal image classification using deep learning models. We present top research methods developed over the years to improve models trained from scratch and transfer learning approaches. We show that when fully trained, a model can first implicitly discriminate the imaging modality and then diagnose the relevant disease. Our developed models were applied to COVID-19 classification from chest X-ray, CT scan, and lung ultrasound image modalities. The model that achieved the highest accuracy correctly maps all input images to their respective modality, then classifies the disease achieving overall 91.07% accuracy.

Bioengineering (Basel, Switzerland)

10.3390/bioengineering9070312 10.1111/exd.13777 10.1109/ACCESS.2020.3016780 10.3390/diagnostics10080565 10.1007/s40747-021-00321-0 10.31083/j.fbl2707198 10.1101/2020.04.24.20078584 10.1109/ACCESS.2020.3010287 10.48550/arXiv.1907.08610 10.1016/j.ibmed.2021.100034 10.3390/bioengineering9040161

A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection.

Recently, the novel coronavirus disease 2019 (COVID-19) has posed many challenges to the research community by presenting grievous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that results in a huge number of mortalities and high morbidities worldwide. Furthermore, the symptoms-based variations in virus type add new challenges for the research and practitioners to combat. COVID-19-infected patients comprise trenchant radiographic visual features, including dry cough, fever, dyspnea, fatigue, etc. Chest X-ray is considered a simple and non-invasive clinical adjutant that performs a key role in the identification of these ocular responses related to COVID-19 infection. Nevertheless, the defined availability of proficient radiologists to understand the X-ray images and the elusive aspects of disease radiographic replies to remnant the biggest bottlenecks in manual diagnosis. To address these issues, the proposed research study presents a hybrid deep learning model for the accurate diagnosing of Delta-type COVID-19 infection using X-ray images. This hybrid model comprises visual geometry group 16 (VGG16) and a support vector machine (SVM), where the VGG16 is accustomed to the identification process, while the SVM is used for the severity-based analysis of the infected people. An overall accuracy rate of 97.37% is recorded for the assumed model. Other performance metrics such as the area under the curve (AUC), precision, F-score, misclassification rate, and confusion matrix are used for validation and analysis purposes. Finally, the applicability of the presumed model is assimilated with other relevant techniques. The high identification rates shine the applicability of the formulated hybrid model in the targeted research domain.

Frontiers in public health

10.3389/fpubh.2022.875971 10.1016/j.compbiomed.2020.103805 10.1016/j.eswa.2020.114054 10.1109/INMIC50486.2020.9318212 10.1007/s10489-020-01902-1 10.1109/MITP.2020.3036820 10.1016/j.eswa.2020.113909 10.1056/NEJMoa2001191 10.1016/j.ijid.2020.01.009 10.1056/NEJMc2001468 10.1016/j.compeleceng.2020.106906 10.32604/cmc.2021.013878 10.1007/s10044-021-00970-4 10.1038/s41598-020-76550-z 10.1093/jamia/ocaa280 10.3390/sym12040651 10.1016/j.engappai.2019.03.021 10.1016/j.advengsoft.2017.05.014 10.1016/j.engappai.2020.103541 10.1038/s41598-020-71294-2 10.22581/muet1982.2101.14 10.1177/0020294020964826

A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging.

The world has witnessed dramatic changes because of the advent of COVID19 in the last few days of 2019. During the last more than two years, COVID-19 has badly affected the world in diverse ways. It has not only affected human health and mortality rate but also the economic condition on a global scale. There is an urgent need today to cope with this pandemic and its diverse effects. Medical imaging has revolutionized the treatment of various diseases during the last four decades. Automated detection and classification systems have proven to be of great assistance to the doctors and scientific community for the treatment of various diseases. In this paper, a novel framework for an efficient COVID-19 classification system is proposed which uses the hybrid feature extraction approach. After preprocessing image data, two types of features i.e., deep learning and handcrafted, are extracted. For Deep learning features, two pre-trained models namely ResNet101 and DenseNet201 are used. Handcrafted features are extracted using Weber Local Descriptor (WLD). The Excitation component of WLD is utilized and features are reduced using DCT. Features are extracted from both models, handcrafted features are fused, and significant features are selected using entropy. Experiments have proven the effectiveness of the proposed model. A comprehensive set of experiments have been performed and results are compared with the existing well-known methods. The proposed technique has performed better in terms of accuracy and time.

Journal of grid computing

10.1007/s10723-022-09615-0 10.1109/ACCESS.2020.2999468 10.1007/s11063-018-09976-2 10.1109/ACCESS.2017.2789324 10.1007/s10723-020-09506-2 10.1007/s10723-021-09594-8 10.1007/s10723-021-09564-0 10.1016/j.compbiomed.2018.03.016 10.1007/s10723-020-09513-3 10.1007/s10723-021-09590-y 10.1016/j.diii.2020.03.014 10.1016/j.chaos.2020.109944 10.1016/j.compbiomed.2021.104453 10.1016/j.chemolab.2020.104054 10.1016/j.bspc.2021.102987 10.1016/j.bbe.2021.05.013 10.1016/j.compbiomed.2021.104306 10.1016/j.eswa.2021.115650 10.1016/j.bspc.2021.102602 10.1016/j.bspc.2021.102588 10.1016/j.clinimag.2021.07.004 10.1016/j.iot.2021.100377 10.1016/j.asoc.2021.107184 10.1016/j.eswa.2021.114883 10.1007/s10489-021-02393-4 10.1007/s10489-020-01867-1 10.1007/s11042-021-11192-5 10.1023/B:VLSI.0000028532.53893.82 10.1109/TPAMI.2009.155 10.1109/T-C.1974.223784 10.1109/TNNLS.2020.2966319 10.1016/j.compbiomed.2020.103792 10.1007/s13246-020-00865-4 10.1007/s13246-020-00952-6 10.1109/ACCESS.2020.2994762

An efficient deep learning-based framework for tuberculosis detection using chest X-ray images.

Early diagnosis of tuberculosis (TB) is an essential and challenging task to prevent disease, decrease mortality risk, and stop transmission to other people. The chest X-ray (CXR) is the top choice for lung disease screening in clinics because it is cost-effective and easily accessible in most countries. However, manual screening of CXR images is a heavy burden for radiologists, resulting in a high rate of inter-observer variances. Hence, proposing a cost-effective and accurate computer aided diagnosis (CAD) system for TB diagnosis is challenging for researchers. In this research, we proposed an efficient and straightforward deep learning network called TBXNet, which can accurately classify a large number of TB CXR images. The network is based on five dual convolutions blocks with varying filter sizes of 32, 64, 128, 256 and 512, respectively. The dual convolution blocks are fused with a pre-trained layer in the fusion layer of the network. In addition, the pre-trained layer is utilized for transferring pre-trained knowledge into the fusion layer. The proposed TBXNet has achieved an accuracy of 98.98%, and 99.17% on Dataset A and Dataset B, respectively. Furthermore, the generalizability of the proposed work is validated against Dataset C, which is based on normal, tuberculous, pneumonia, and COVID-19 CXR images. The TBXNet has obtained the highest results in Precision (95.67%), Recall (95.10%), F1-score (95.38%), and Accuracy (95.10%), which is comparatively better than all other state-of-the-art methods.

Tuberculosis (Edinburgh, Scotland)

10.1016/j.tube.2022.102234

Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19.

To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from 4 test sets, including 3 from the United States (patients hospitalized at an academic medical center (N = 154), patients hospitalized at a community hospital (N = 113), and outpatients (N = 108)) and 1 from Brazil (patients at an academic medical center emergency department (N = 303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson R). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. Tuning the deep learning model with outpatient data showed high model performance in 2 United States hospitalized patient datasets (R = 0.88 and R = 0.90, compared to baseline R = 0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (R = 0.86 and R = 0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. A deep learning model that extracts a COVID-19 severity score on CXRs showed generalizable performance across multiple populations from 2 continents, including outpatients and hospitalized patients.

Medicine

10.1097/MD.0000000000029587

Ftl-CoV19: A Transfer Learning Approach to Detect COVID-19.

COVID-19 is an infectious and contagious disease caused by the new coronavirus. The total number of cases is over 19 million and continues to grow. A common symptom noticed among COVID-19 patients is lung infection that results in breathlessness, and the lack of essential resources such as testing, oxygen, and ventilators enhances its severity. Chest X-ray can be used to design and develop a COVID-19 detection mechanism for a quicker diagnosis using AI and machine learning techniques. Due to this silver lining, various new COVID-19 detection techniques and prediction models have been introduced in recent times based on chest radiography images. However, due to a high level of unpredictability and the absence of essential data, standard models have showcased low efficiency and also suffer from overheads and complexities. This paper proposes a model fine tuning transfer learning-coronavirus 19 (Ftl-CoV19) for COVID-19 detection through chest X-rays, which embraces the ideas of transfer learning in pretrained VGG16 model with including combination of convolution, max pooling, and dense layer at different stages of model. Ftl-CoV19 reported promising experimental results; it observed training and validation accuracy of 98.82% and 99.27% with precision of 100%, recall of 98%, and F1 score of 99%. These results outperformed other conventional state of arts such as CNN, ResNet50, InceptionV3, and Xception.

Computational intelligence and neuroscience

10.1155/2022/1953992 10.15557/pimr.2020.0024 10.1016/j.genrep.2020.100756 10.1109/TAI.2021.3062771 10.1109/tmi.2020.2995508 10.1109/ACCESS.2020.2997311 10.1109/RBME.2020.2987975 10.1109/CANDO-EPE51100.2020.9337794 10.1109/TCYB.2019.2950779 10.1016/j.numecd.2020.07.031 10.1155/2020/9756518 10.1101/2020.10.13.20212035 10.1101/2020.10.13.20212035 10.1109/mpuls.2020.3008354 10.1109/access.2020.3009328 10.1109/tmi.2020.2993291 10.1109/tmi.2020.2995965 10.1109/ACCESS.2018.2814605 10.1016/j.imu.2020.100360 10.1109/ACCESS.2019.2946000 10.17632/9xkhgts2s6.1 10.1007/s10489-020-01826-w 10.48550/arXiv.1409.1556 10.3390/s19163556 10.1007/s11042-020-10038-w

Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning.

Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED. This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S. A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression. Overall ICC was 0.820 (95% CI 0.790-0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861-0.920) for the neural network and 0.936 (95% CI 0.918-0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906). The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.

BMC infectious diseases

10.1186/s12879-022-07617-7 10.1136/bmj.m2426 10.1007/s11547-020-01232-9 10.1016/j.ijid.2020.05.021 10.1186/s41747-020-00195-w 10.1148/radiol.2021219021 10.1148/ryct.2020200028 10.1186/s43055-020-00296-x 10.1148/ryct.2020200337 10.1148/radiol.2021219022 10.1136/bmj.m1328 10.1016/j.jiph.2020.06.028 10.1109/RBME.2020.2987975 10.1038/s41598-021-93719-2 10.1038/s42256-021-00307-0 10.1148/radiol.2020202944 10.1148/ryai.2020200079 10.1371/journal.pone.0236621 10.1148/ryct.2020200280 10.1148/ryai.2020200029 10.1001/jamanetworkopen.2021.41096 10.1109/TPAMI.2018.2858826 10.1371/journal.pmed.1002707 10.1016/j.chest.2020.04.003 10.1148/ryai.2020190043 10.1016/j.compbiomed.2021.104665 10.18280/ts.370313

Simplified Transfer Learning for Chest Radiography Models Using Less Data.

Background Developing deep learning models for radiology requires large data sets and substantial computational resources. Data set size limitations can be further exacerbated by distribution shifts, such as rapid changes in patient populations and standard of care during the COVID-19 pandemic. A common partial mitigation is transfer learning by pretraining a "generic network" on a large nonmedical data set and then fine-tuning on a task-specific radiology data set. Purpose To reduce data set size requirements for chest radiography deep learning models by using an advanced machine learning approach (supervised contrastive [SupCon] learning) to generate chest radiography networks. Materials and Methods SupCon helped generate chest radiography networks from 821 544 chest radiographs from India and the United States. The chest radiography networks were used as a starting point for further machine learning model development for 10 prediction tasks (eg, airspace opacity, fracture, tuberculosis, and COVID-19 outcomes) by using five data sets comprising 684 955 chest radiographs from India, the United States, and China. Three model development setups were tested (linear classifier, nonlinear classifier, and fine-tuning the full network) with different data set sizes from eight to 8

Radiology

10.1148/radiol.212482

COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence.

COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as possible. Due to time constraints and the expertise of radiologists, manually diagnosing this infection from chest X-ray images is a difficult and time-consuming process. Artificial intelligence techniques have had a significant impact on medical image analysis and have also introduced several techniques for COVID-19 diagnosis. Deep learning and explainable AI have shown significant popularity among AL techniques for COVID-19 detection and classification. In this work, we propose a deep learning and explainable AI technique for the diagnosis and classification of COVID-19 using chest X-ray images. Initially, a hybrid contrast enhancement technique is proposed and applied to the original images that are later utilized for the training of two modified deep learning models. The deep transfer learning concept is selected for the training of pretrained modified models that are later employed for feature extraction. Features of both deep models are fused using improved canonical correlation analysis that is further optimized using a hybrid algorithm named Whale-Elephant Herding. Through this algorithm, the best features are selected and classified using an extreme learning machine (ELM). Moreover, the modified deep models are utilized for Grad-CAM visualization. The experimental process was conducted on three publicly available datasets and achieved accuracies of 99.1, 98.2, and 96.7%, respectively. Moreover, the ablation study was performed and showed that the proposed accuracy is better than the other methods.

Computational intelligence and neuroscience

10.1155/2022/4254631 10.3390/diagnostics12030741 10.1038/s41598-022-10723-w 10.1371/journal.pone.0246772 10.1016/j.genhosppsych.2020.07.006 10.1016/j.eng.2020.04.010 10.1002/1096-9071(200103)63:3<259::aid-jmv1010>3.0.co;2-x 10.1016/j.eswa.2020.114054 10.7326/m20-1382 10.1007/s00330-021-07715-1 10.1109/tmi.2016.2553401 10.1109/trpms.2019.2896399 10.1049/trit.2019.0028 10.1016/j.compag.2021.106081 10.3390/e23060667 10.1007/978-3-030-27272-2_14 10.1007/s11042-019-08111-0 10.1080/03772063.2017.1331757 10.1007/s11517-020-02302-w 10.1016/j.neucom.2021.03.035 10.1109/tpami.2016.2644615 10.1016/j.artmed.2021.102114 10.1109/tip.2021.3058783 10.3390/s21020455 10.1038/s41598-021-95680-6 10.36227/techrxiv.15135846.v1 10.1109/jbhi.2021.3074893 10.3390/healthcare9091099 10.1007/s00521-020-05636-6 10.3390/a14110337 10.3390/s21165657 10.1038/s42256-021-00338-7 10.1109/cbms52027.2021.00103 10.1016/j.advengsoft.2016.01.008 10.1109/iscbi.2015.8 10.1109/iccv.2017.74

A Deep Learning Approach to Identify Chest Computed Tomography Features for Prediction of SARS-CoV-2 Infection Outcomes.

There is still an urgent need to develop effective treatments to help minimize the cases of severe COVID-19. A number of tools have now been developed and applied to address these issues, such as the use of non-contrast chest computed tomography (CT) for evaluation and grading of the associated lung damage. Here we used a deep learning approach for predicting the outcome of 1078 patients admitted into the Baqiyatallah Hospital in Tehran, Iran, suffering from COVID-19 infections in the first wave of the pandemic. These were classified into two groups of non-severe and severe cases according to features on their CT scans with accuracies of approximately 0.90. We suggest that incorporation of molecular and/or clinical features, such as multiplex immunoassay or laboratory findings, will increase accuracy and sensitivity of the model for COVID-19 -related predictions.

Methods in molecular biology (Clifton, N.J.)

10.1007/978-1-0716-2395-4_30 10.1039/D0LC01156H 10.1016/j.cie.2021.107235 10.1016/S0140-6736(20)30360-3 10.7150/ijms.50568 10.1186/s12879-021-06528-3 10.1148/radiol.2020200330 10.1148/radiol.2020200343 10.1007/978-3-030-59261-5_24 10.2214/AJR.20.22975 10.1148/radiol.2020200230 10.1021/acsnano.0c02624 10.5114/pjr.2020.98009 10.1136/bmjhci-2021-100389 10.1148/ryct.2021200510 10.1038/s41598-021-99015-3 10.3390/diagnostics11091712 10.1007/978-3-030-71697-4_11 10.1148/radiol.2462070712 10.1186/s12879-019-4592-0 10.1148/radiol.2363040958 10.1016/j.ejrad.2021.109583 10.1038/s41467-020-20657-4 10.3348/kjr.2020.0994 10.1016/j.compbiomed.2021.104304 10.3348/kjr.2020.1104 10.1038/s41598-021-93719-2

Feature-level ensemble approach for COVID-19 detection using chest X-ray images.

Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), also known as the coronavirus disease 2019 (COVID-19), has threatened many human beings around the world and capsized economies at unprecedented magnitudes. Therefore, the detection of this disease using chest X-ray modalities has played a pivotal role in producing fast and accurate medical diagnoses, especially in countries that are unable to afford laboratory testing kits. However, identifying and distinguishing COVID-19 from virtually similar thoracic abnormalities utilizing medical images is challenging because it is time-consuming, demanding, and susceptible to human-based errors. Therefore, artificial-intelligence-driven automated diagnoses, which excludes direct human intervention, may potentially be used to achieve consistently accurate performances. In this study, we aimed to (i) obtain a customized dataset composed of a relatively small number of images collected from publicly available datasets; (ii) present the efficient integration of the shallow handcrafted features obtained from local descriptors, radiomics features specialized for medical images, and deep features aggregated from pre-trained deep learning architectures; and (iii) distinguish COVID-19 patients from healthy controls and pneumonia patients using a collection of conventional machine learning classifiers. By conducting extensive experiments, we demonstrated that the feature-based ensemble approach provided the best classification metrics, and this approach explicitly outperformed schemes that used only either local, radiomic, or deep features. In addition, our proposed method achieved state-of-the-art multi-class classification results compared to the baseline reference for the currently available COVID-19 datasets.

PloS one

10.1371/journal.pone.0268430 10.1056/NEJMoa2002032 10.1128/JCM.00512-20 10.1093/cid/ciaa310 10.1002/jmv.26699 10.1038/nature21056 10.1002/jmri.26534 10.1016/j.bspc.2019.101678 10.1016/j.compmedimag.2019.101673 10.1016/j.eswa.2018.04.021 10.1109/ACCESS.2019.2900127 10.1148/radiol.2019182716 10.1016/j.media.2018.03.006 10.3390/app9194130 10.1080/07391102.2020.1767212 10.1007/s40846-020-00529-4 10.1007/s13246-020-00865-4 10.1109/TMI.2020.2993291 10.3390/s18030699 10.1109/ACCESS.2019.2917266 10.1109/ACCESS.2019.2922691 10.1109/JBHI.2017.2775662 10.1016/j.scs.2020.102589 10.1016/j.eswa.2020.114054 10.1007/s10489-020-01829-7 10.3390/electronics9091388 10.1016/j.mehy.2020.109761 10.1007/s10489-020-01900-3 10.1038/s41598-020-76550-z 10.1016/j.compbiomed.2020.103792 10.1023/A:1011139631724 10.1038/ncomms5006 10.1158/1078-0432.CCR-14-0990 10.1007/s10115-006-0013-y 10.1016/j.patcog.2006.12.019 10.1016/j.jneumeth.2015.09.019 10.1016/j.patcog.2006.06.008 10.4310/SII.2009.v2.n3.a8 10.1016/j.neuroimage.2012.09.065 10.1016/j.engappai.2015.04.003 10.7717/peerj-cs.551 10.1016/j.isatra.2022.02.033 10.1117/1.JMI.4.4.041305

Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques.

Deep learning provides the healthcare industry with the ability to analyse data at exceptional speeds without compromising on accuracy. These techniques are applicable to healthcare domain for accurate and timely prediction. Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. Lung diseases such as tuberculosis (TB), bacterial and viral pneumonias, and COVID-19 are not predicted accurately due to availability of very few samples for either of the lung diseases. The disease could be easily diagnosed using X-ray or CT scan images. But the number of images available for each of the disease is not as equally as other resulting in imbalance nature of input data. Conventional supervised machine learning methods do not achieve higher accuracy when trained using a lesser amount of COVID-19 data samples. Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Data augmentation helped reduce overfitting when training a deep neural network. The SMOTE (Synthetic Minority Oversampling Technique) algorithm is used for the purpose of balancing the classes. The novelty in this research work is to apply combined data augmentation and class balance techniques before classification of tuberculosis, pneumonia, and COVID-19. The classification accuracy obtained with the proposed multi-level classification after training the model is recorded as 97.4% for TB and pneumonia and 88% for bacterial, viral, and COVID-19 classifications. The proposed multi-level classification method produced is ~8 to ~10% improvement in classification accuracy when compared with the existing methods in this area of research. The results reveal the fact that the proposed system is scalable to growing medical data and classifies lung diseases and its sub-types in less time with higher accuracy.

Medical & biological engineering & computing

10.1007/s11517-022-02632-x 10.1080/01431169508954507 10.1007/s42979-021-00695-5 10.3390/app10093233 10.1016/j.cell.2018.02.010 10.3390/app8101715 10.1111/j.1440-1843.2006.00947.x 10.1007/s40747-020-00199-4 10.1016/j.bbe.2020.08.008 10.1613/jair.953 10.1504/IJKESDP.2011.039875 10.1613/jair.1.11192 10.1016/j.eswa.2021.114986 10.1016/j.knosys.2021.107269 10.1016/j.ins.2021.03.041 10.1109/TMI.2020.2993291 10.1002/ima.22613 10.1016/j.compbiomed.2021.105134 10.3390/app10020559 10.1016/j.patrec.2021.08.018

RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights.

Myocarditis is heart muscle inflammation that is becoming more prevalent these days, especially with the prevalence of COVID-19. Noninvasive imaging cardiac magnetic resonance (CMR) can be used to diagnose myocarditis, but the interpretation is time-consuming and requires expert physicians. Computer-aided diagnostic systems can facilitate the automatic screening of CMR images for triage. This paper presents an automatic model for myocarditis classification based on a deep reinforcement learning approach called as reinforcement learning-based myocarditis diagnosis combined with population-based algorithm (RLMD-PA) that we evaluated using the Z-Alizadeh Sani myocarditis dataset of CMR images prospectively acquired at Omid Hospital, Tehran. This model addresses the imbalanced classification problem inherent to the CMR dataset and formulates the classification problem as a sequential decision-making process. The policy of architecture is based on convolutional neural network (CNN). To implement this model, we first apply the artificial bee colony (ABC) algorithm to obtain initial values for RLMD-PA weights. Next, the agent receives a sample at each step and classifies it. For each classification act, the agent gets a reward from the environment in which the reward of the minority class is greater than the reward of the majority class. Eventually, the agent finds an optimal policy under the guidance of a particular reward function and a helpful learning environment. Experimental results based on standard performance metrics show that RLMD-PA has achieved high accuracy for myocarditis classification, indicating that the proposed model is suitable for myocarditis diagnosis.

Contrast media & molecular imaging

10.1155/2022/8733632 10.1056/nejmra0800028 10.1016/j.humpath.2005.07.009 10.1007/978-3-030-92238-2_57 10.1007/s00500-014-1334-5 10.1007/s10479-011-0894-3 10.1016/j.knosys.2017.11.029 10.1007/11538059_91 10.1145/1007730.1007735 10.1007/s10489-020-01637-z 10.1109/tkde.2005.95 10.1016/j.knosys.2015.10.012 10.1109/3477.764879 10.1016/j.asoc.2007.05.007 10.1609/aaai.v33i01.33013959 10.1016/j.jcmg.2009.09.023 10.12688/f1000research.14857.1 10.1186/s12968-019-0550-7 10.1161/circimaging.118.007598 10.1007/bf03086308 10.1016/j.acra.2013.01.004 10.1186/s12968-017-0419-6 10.1007/bf00994018 10.1001/jama.2016.7653 10.1007/978-1-4419-9326-7_5 10.1109/72.286925 10.1016/j.advengsoft.2013.12.007 10.1007/978-3-642-12538-6_6 10.1016/j.advengsoft.2016.01.008

Non-iterative learning machine for identifying CoViD19 using chest X-ray images.

CoViD19 is a novel disease which has created panic worldwide by infecting millions of people around the world. The last significant variant of this virus, called as omicron, contributed to majority of cases in the third wave across globe. Though lesser in severity as compared to its predecessor, the delta variant, this mutation has shown higher communicable rate. This novel virus with symptoms of pneumonia is dangerous as it is communicable and hence, has engulfed entire world in a very short span of time. With the help of machine learning techniques, entire process of detection can be automated so that direct contacts can be avoided. Therefore, in this paper, experimentation is performed on CoViD19 chest X-ray images using higher order statistics with iterative and non-iterative models. Higher order statistics provide a way of analyzing the disturbances in the chest X-ray images. The results obtained are quite good with 96.64% accuracy using a non-iterative model. For fast testing of the patients, non-iterative model is preferred because it has advantage over iterative model in terms of speed. Comparison with some of the available state-of-the-art methods and some iterative methods proves efficacy of the work.

Scientific reports

10.1038/s41598-022-15268-6 10.3346/jkms.2020.35.e150 10.1001/jama.2021.2927 10.1016/j.clinimag.2020.04.010 10.1016/j.jcv.2020.104359 10.1016/j.jcv.2020.104356 10.5582/bst.2020.01047 10.1016/j.ajem.2020.09.032 10.1007/s11046-021-00528-2 10.1007/s13246-020-00865-4 10.1109/JBHI.2020.3037127 10.1007/s10096-020-03901-z 10.1109/TIP.2021.3058783 10.1007/s10489-020-01826-w 10.1007/s12559-020-09787-5 10.1016/j.media.2020.101824 10.1016/j.chaos.2020.110495 10.1007/s10140-020-01886-y 10.1016/j.bspc.2021.102588 10.1016/j.compeleceng.2020.106960 10.1016/j.sysarc.2020.101830 10.1109/ACCESS.2020.3016780 10.1016/j.measurement.2020.108288 10.1016/j.asoc.2020.106912 10.1080/07391102.2020.1788642 10.1016/j.media.2020.101794 10.1007/s00521-020-05437-x 10.1109/5254.708428 10.1016/j.ejor.2017.08.040 10.1049/el.2017.0023 10.1016/j.neucom.2005.12.126 10.1016/j.neucom.2007.02.009 10.1016/j.neucom.2007.10.008 10.1109/TSMCB.2011.2168604 10.1007/s13369-020-04566-8 10.4108/eai.13-7-2018.163575 10.1007/s11042-019-08537-6 10.17148/IARJSET.2016.3119 10.1038/s41598-020-79139-8 10.1016/j.eng.2020.04.010 10.1038/s41598-019-56847-4

Detection of COVID-19 using deep learning techniques and classification methods.

Since the patient is not quarantined during the conclusion of the Polymerase Chain Reaction (PCR) test used in the diagnosis of COVID-19, the disease continues to spread. In this study, it was aimed to reduce the duration and amount of transmission of the disease by shortening the diagnosis time of COVID-19 patients with the use of Computed Tomography (CT). In addition, it is aimed to provide a decision support system to radiologists in the diagnosis of COVID-19. In this study, deep features were extracted with deep learning models such as ResNet-50, ResNet-101, AlexNet, Vgg-16, Vgg-19, GoogLeNet, SqueezeNet, Xception on 1345 CT images obtained from the radiography database of Siirt Education and Research Hospital. These deep features are given to classification methods such as Support Vector Machine (SVM), k Nearest Neighbor (kNN), Random Forest (RF), Decision Trees (DT), Naive Bayes (NB), and their performance is evaluated with test images. Accuracy value, F1-score and ROC curve were considered as success criteria. According to the data obtained as a result of the application, the best performance was obtained with ResNet-50 and SVM method. The accuracy was 96.296%, the F1-score was 95.868%, and the AUC value was 0.9821. The deep learning model and classification method examined in this study and found to be high performance can be used as an auxiliary decision support system by preventing unnecessary tests for COVID-19 disease.

Information processing & management

10.1016/j.ipm.2022.103025 10.1101/2020.03.12.20027185

Computational Intelligence-Based Method for Automated Identification of COVID-19 and Pneumonia by Utilizing CXR Scans.

Chest X-ray (CXR) scans are emerging as an important diagnostic tool for the early spotting of COVID and other significant lung diseases. The recognition of visual symptoms is difficult and can take longer time by radiologists as CXR provides various signs of viral infection. Therefore, artificial intelligence-based method for automated identification of COVID by utilizing X-ray images has been found to be very promising. In the era of deep learning, effective utilization of existing pretrained generalized models is playing a decisive role in terms of time and accuracy. In this paper, the benefits of weights of existing pretrained model VGG16 and InceptionV3 have been taken. Base model has been created using pretrained models (VGG16 and InceptionV3). The last fully connected (FC) layer has been added as per the number of classes for classification of CXR in binary and multi-class classification by appropriately using transfer learning. Finally, combination of layers is made by integrating the FC layer weights of both the models (VGG16 and InceptionV3). The image dataset used for experimentation consists of healthy, COVID, pneumonia viral, and pneumonia bacterial. The proposed weight fusion method has outperformed the existing models in terms of accuracy, achieved 99.5% accuracy in binary classification over 20 epochs, and 98.2% accuracy in three-class classification over 100 epochs.

Computational intelligence and neuroscience

10.1155/2022/7124199 10.1016/j.ijid.2020.01.009 10.4018/ijehmc.20220701.oa4 10.1111/exsy.12749 10.1109/jiot.2018.2802898 10.1109/tkde.2009.191 10.1186/s40537-016-0043-6 10.1109/access.2018.2845399 10.1016/j.eng.2020.04.010 10.1080/07391102.2020.1767212 10.1016/j.compbiomed.2020.103792 10.1109/access.2020.2994762 10.1016/j.mehy.2020.109761 10.1109/tmi.2020.2993291 10.1007/s40846-020-00529-4 10.3390/app10020559 10.1016/j.measurement.2019.05.076 10.1155/2020/8828855 10.1016/j.cmpb.2020.105532 10.1016/j.imu.2020.100360 10.1016/j.imu.2020.100412 10.3233/xst-200720 10.1016/j.asoc.2020.106580 10.1016/j.chaos.2020.110071 10.3390/life11111118 10.1007/s10489-020-01826-w 10.1109/access.2021.3095312 10.1109/ssci50451.2021.9659919 10.1155/2021/6919483 10.1155/2021/8828404 10.1109/CVPR.2016.308 10.1016/j.jpha.2020.03.001 10.3238/arztebl.2014.0181 10.1016/j.chaos.2020.109947 10.1038/s41598-020-74539-2 10.1155/2020/8889023 10.1109/iceiec49280.2020.9152329 10.1016/j.compbiomed.2020.103869 10.1007/978-3-030-01424-7_27 10.3906/elk-2105-243 10.1016/j.compeleceng.2022.108028 10.1155/2022/8549707 10.3390/s22062278

PCA-Based Incremental Extreme Learning Machine (PCA-IELM) for COVID-19 Patient Diagnosis Using Chest X-Ray Images.

Novel coronavirus 2019 has created a pandemic and was first reported in December 2019. It has had very adverse consequences on people's daily life, healthcare, and the world's economy as well. According to the World Health Organization's most recent statistics, COVID-19 has become a worldwide pandemic, and the number of infected persons and fatalities growing at an alarming rate. It is highly required to have an effective system to early detect the COVID-19 patients to curb the further spreading of the virus from the affected person. Therefore, to early identify positive cases in patients and to support radiologists in the automatic diagnosis of COVID-19 from X-ray images, a novel method PCA-IELM is proposed based on principal component analysis (PCA) and incremental extreme learning machine. The suggested method's key addition is that it considers the benefits of PCA and the incremental extreme learning machine. Further, our strategy PCA-IELM reduces the input dimension by extracting the most important information from an image. Consequently, the technique can effectively increase the COVID-19 patient prediction performance. In addition to these, PCA-IELM has a faster training speed than a multi-layer neural network. The proposed approach was tested on a COVID-19 patient's chest X-ray image dataset. The experimental results indicate that the proposed approach PCA-IELM outperforms PCA-SVM and PCA-ELM in terms of accuracy (98.11%), precision (96.11%), recall (97.50%), F1-score (98.50%), etc., and training speed.

Computational intelligence and neuroscience

10.1155/2022/9107430 10.1016/j.neucom.2012.02.042 10.1007/s00521-014-1567-3 10.1016/j.jvcir.2019.05.016 10.1007/s00521-020-05204-y 10.1007/s10586-021-03282-8 10.1007/s11063-012-9253-x 10.3390/sym11010001 10.1007/s12559-014-9259-y 10.1016/j.neucom.2007.02.009 10.1109/UT.2017.7890275 10.1109/tgrs.2017.2743102 10.1016/j.asoc.2021.107482 10.1016/j.cageo.2021.104877 10.1007/s13042-015-0419-5 10.1016/j.neucom.2005.12.126 10.1007/s13042-020-01232-1 10.1016/j.neucom.2007.07.025 10.1016/j.eswa.2016.08.052 10.3390/en12071223 10.1504/ijmso.2018.096451 10.1007/s11042-018-7093-z 10.1080/07391102.2022.2034668 10.1016/B978-0-12-816718-2.00016-6 10.1080/10798587.2017.1316071 10.1016/j.scs.2022.103713 10.1155/2016/7293278 10.1016/j.engappai.2018.07.002 10.1007/s13042-019-01001-9 10.1007/s11694-008-9043-3 10.1016/j.neucom.2020.04.098 10.1080/21642583.2020.1759156 10.32604/cmc.2021.016957 10.1007/s11042-019-07978-3 10.1016/j.compbiomed.2020.103792 10.1007/s13246-020-00865-4 10.3389/fmed.2020.608525 10.33889/ijmems.2020.5.4.052 10.48550/arXiv.2003.11055 10.48550/arXiv.2110.04160 10.1101/2020.02.23.20026930 10.1007/s00330-021-07715-1 10.1016/j.scs.2020.102589 10.1016/j.eng.2020.04.010

CAD systems for COVID-19 diagnosis and disease stage classification by segmentation of infected regions from CT images.

Here propose a computer-aided diagnosis (CAD) system to differentiate COVID-19 (the coronavirus disease of 2019) patients from normal cases, as well as to perform infection region segmentation along with infection severity estimation using computed tomography (CT) images. The developed system facilitates timely administration of appropriate treatment by identifying the disease stage without reliance on medical professionals. So far, this developed model gives the most accurate, fully automatic COVID-19 real-time CAD framework. The CT image dataset of COVID-19 and non-COVID-19 individuals were subjected to conventional ML stages to perform binary classification. In the feature extraction stage, SIFT, SURF, ORB image descriptors and bag of features technique were implemented for the appropriate differentiation of chest CT regions affected with COVID-19 from normal cases. This is the first work introducing this concept for COVID-19 diagnosis application. The preferred diverse database and selected features that are invariant to scale, rotation, distortion, noise etc. make this framework real-time applicable. Also, this fully automatic approach which is faster compared to existing models helps to incorporate it into CAD systems. The severity score was measured based on the infected regions along the lung field. Infected regions were segmented through a three-class semantic segmentation of the lung CT image. Using severity score, the disease stages were classified as mild if the lesion area covers less than 25% of the lung area; moderate if 25-50% and severe if greater than 50%. Our proposed model resulted in classification accuracy of 99.7% with a PNN classifier, along with area under the curve (AUC) of 0.9988, 99.6% sensitivity, 99.9% specificity and a misclassification rate of 0.0027. The developed infected region segmentation model gave 99.47% global accuracy, 94.04% mean accuracy, 0.8968 mean IoU (intersection over union), 0.9899 weighted IoU, and a mean Boundary F1 (BF) contour matching score of 0.9453, using Deepabv3+ with its weights initialized using ResNet-50. The developed CAD system model is able to perform fully automatic and accurate diagnosis of COVID-19 along with infected region extraction and disease stage identification. The ORB image descriptor with bag of features technique and PNN classifier achieved the superior classification performance.

BMC bioinformatics

10.1186/s12859-022-04818-4 10.1186/s12859-021-04083-x 10.1016/j.patrec.2020.10.001 10.1007/978-3-030-01234-2_49 10.1007/s11042-022-12608-6 10.1016/j.imu.2020.100427 10.1007/s10916-020-01562-1 10.1038/s41598-020-79139-8 10.1038/s41598-019-56847-4 10.1016/j.bbe.2021.05.013 10.1038/s41598-020-79139-8 10.1371/journal.pone.0235187 10.1038/s41598-020-79139-8 10.1038/s41591-020-0931-3 10.3390/diagnostics10110901 10.1109/TMI.2020.2996645 10.1016/j.eswa.2021.114848 10.1371/journal.pone.0252384.t001 10.1371/journal.pone.0236618 10.3389/fmed.2020.557453 10.1007/s00330-020-07033-y 10.1016/j.cell.2020.04.045 10.1007/s12530-021-09386-1 10.1007/s00371-020-01814-8 10.1017/S1431927621001653 10.1007/978-981-15-5697-5_11 10.1007/s11760-020-01759-4 10.3390/bdcc5040053 10.1007/s10489-021-02731-6 10.1007/s00330-021-08049-8 10.3389/fmed.2020.608525 10.1117/1.JMI.8.S1.017502.full

FWLICM-Deep Learning: Fuzzy Weighted Local Information C-Means Clustering-Based Lung Lobe Segmentation with Deep Learning for COVID-19 Detection.

Coronavirus (COVID-19) creates an extensive range of respiratory contagions, and it is a kind of ribonucleic acid (RNA) virus, which affects both animals and humans. Moreover, COVID-19 is a new disease, which produces contamination in upper respiration alterritory and lungs. The new COVID is a rapidly spreading pathogen globally, and it threatens billions of humans' lives. However, it is significant to identify positive cases in order to avoid the spread of plague and to speedily treat infected patients. Hence, in this paper, the WSCA-based RMDL approach is devised for COVID-19 prediction by means of chest X-ray images. Moreover, Fuzzy Weighted Local Information C-Means (FWLICM) approach is devised in order to segment lung lobes. The developed FWLICM method is designed by modifying the Fuzzy Local Information C-Means (FLICM) technique. Additionally, random multimodel deep learning (RMDL) classifier is utilized for the COVID-19 prediction process. The new optimization approach, named water sine cosine algorithm (WSCA), is devised in order to obtain an effective prediction. The developed WSCA is newly designed by incorporating sine cosine algorithm (SCA) and water cycle algorithm (WCA). The developed WSCA-driven RMDL approach outperforms other COVID-19 prediction techniques with regard to accuracy, specificity, sensitivity, and dice score of 92.41%, 93.55%, 92.14%, and 90.02%.

Journal of digital imaging

10.1007/s10278-022-00667-y 10.1016/j.compbiomed.2020.103805 10.1016/j.cmpb.2020.105581 10.1016/j.compbiomed.2020.103792 10.1056/NEJMc2001468 10.1007/s12098-020-03263-6 10.1016/S0140-6736(20)30522-5 10.1038/s41368-020-0075-9 10.1148/radiol.2020200490 10.32098/mltj.01.2016.06 10.1053/j.jfas.2020.11.003 10.1016/j.media.2017.07.005 10.1109/ACCESS.2017.2788044 10.1016/j.cmpb.2018.04.005 10.1007/s10462-018-9641-3 10.1016/j.measurement.2019.05.076 10.46253/j.mr.v3i2.a4 10.1080/14737159.2020.1757437 10.1109/TIP.2010.2040763 10.1016/j.compstruc.2012.07.010 10.1016/j.knosys.2015.12.022 10.1016/j.eswa.2020.114054 10.1007/s13246-020-00952-6

Self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation.

Although deep learning-based computer-aided diagnosis systems have recently achieved expert-level performance, developing a robust model requires large, high-quality data with annotations that are expensive to obtain. This situation poses a conundrum that annually-collected chest x-rays cannot be utilized due to the absence of labels, especially in deprived areas. In this study, we present a framework named distillation for self-supervision and self-train learning (DISTL) inspired by the learning process of the radiologists, which can improve the performance of vision transformer simultaneously with self-supervision and self-training through knowledge distillation. In external validation from three hospitals for diagnosis of tuberculosis, pneumothorax, and COVID-19, DISTL offers gradually improved performance as the amount of unlabeled data increase, even better than the fully supervised model with the same amount of labeled data. We additionally show that the model obtained with DISTL is robust to various real-world nuisances, offering better applicability in clinical setting.

Nature communications

10.1038/s41467-022-31514-x 10.1001/jama.2016.17216 10.1038/s41591-018-0107-6 10.1038/s41591-018-0029-3 10.1016/j.jacr.2017.12.028 10.1186/s41747-018-0061-6 10.1148/radiol.2017162326 10.1038/s41598-019-42557-4 10.1371/journal.pone.0221339 10.1016/S2589-7500(21)00116-3 10.2174/1573405617666210127154257 10.1016/j.media.2019.101539 10.1109/TMI.2020.2995518 10.1016/j.media.2020.101797

Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs.

Coronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs. Two bone suppression methods (Gusarev Bone suppression of external test data was found to significantly (P<0.05) improve AUC for one classifier architecture [from 0.698 (non-suppressed) to 0.732 (Rajaraman-suppressed)]. For the other classifier architecture, suppression did not significantly (P>0.05) improve or worsen classifier performance. Rajaraman suppression significantly improved classification performance in one classification architecture, and did not significantly worsen classifier performance in the other classifier architecture. This research could be extended to explore the impact of bone suppression on classification of different lung pathologies, and the effect of other image enhancement techniques on classifier performance.

Quantitative imaging in medicine and surgery

10.21037/qims-21-791 10.1016/S0140-6736(20)30183-5 10.1016/S2213-2600(20)30076-X 10.2807/1560-7917.ES.2021.26.24.2100509 10.1016/S0140-6736(21)01358-1 10.1056/NEJMoa2002032 10.1148/radiol.2020200642 10.1016/j.radi.2020.10.018 10.1007/s11263-015-0816-y 10.1155/2018/7068349 10.1038/s41598-020-76550-z 10.1007/s10489-020-01829-7 10.1007/s10489-020-01829-7 10.1097/RLI.0000000000000748 10.3348/kjr.2020.0536 10.1016/j.patrec.2021.06.021 10.3390/diagnostics11050840 10.21037/qims-20-1230 10.1002/mp.14371 10.1007/s11548-015-1278-y 10.1109/TMI.2006.871549 10.1016/j.ejrad.2009.03.046 10.1148/radiol.11100153 10.2214/ajr.174.1.1740071 10.7937/91ah-v663 10.1148/radiol.2021203957 10.1148/ryai.2019180041 10.1109/TIP.2003.819861 10.1016/j.compbiomed.2021.104319 10.1038/s42256-021-00307-0 10.1038/s41598-020-74539-2 10.2307/2531595 10.1371/journal.pone.0254809 10.15585/mmwr.mm7015e2 10.1038/s41598-021-83237-6

Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images.

Lung infections caused by bacteria and viruses are infectious and require timely screening and isolation, and different types of pneumonia require different treatment plans. Therefore, finding a rapid and accurate screening method for lung infections is critical. To achieve this goal, we proposed a multi-branch fusion auxiliary learning (MBFAL) method for pneumonia detection from chest X-ray (CXR) images. The MBFAL method was used to perform two tasks through a double-branch network. The first task was to recognize the absence of pneumonia (normal), COVID-19, other viral pneumonia and bacterial pneumonia from CXR images, and the second task was to recognize the three types of pneumonia from CXR images. The latter task was used to assist the learning of the former task to achieve a better recognition effect. In the process of auxiliary parameter updating, the feature maps of different branches were fused after sample screening through label information to enhance the model's ability to recognize case of pneumonia without impacting its ability to recognize normal cases. Experiments show that an average classification accuracy of 95.61% is achieved using MBFAL. The single class accuracy for normal, COVID-19, other viral pneumonia and bacterial pneumonia was 98.70%, 99.10%, 96.60% and 96.80%, respectively, and the recall was 97.20%, 98.60%, 96.10% and 89.20%, respectively, using the MBFAL method. Compared with the baseline model and the model constructed using the above methods separately, better results for the rapid screening of pneumonia were achieved using MBFAL.

Computers in biology and medicine

10.1016/j.compbiomed.2022.105732 10.1109/TMI.2020.3040950 10.1128/jcm.02589-20 10.1186/s13054-015-1083-6 10.1002/jmv.25674 10.2807/1560-7917.es.2020.25.3.2000045 10.1148/radiol.2020200432 10.1148/radiol.2020200241 10.1016/j.chest.2020.04.003 10.1148/radiol.2020200343 10.1016/j.clinimag.2020.11.004 10.1148/rg.2018170048 10.1148/ryct.2020200034 10.1109/ICESC51422.2021.9532943 10.1016/j.compmedimag.2016.11.004 10.1016/j.crad.2018.12.015 10.1109/TMI.2020.2994908 10.1109/TPAMI.2021.3054719 10.1109/CVPR.2019.00197 10.1109/CVPR.2019.00332 10.1023/A:1007379606734 10.1109/ICCV.2019.00649 10.1007/s13246-020-00865-4 10.1038/s41598-020-76550-z 10.1007/s00500-020-05424-3 10.1109/ICCC51575.2020.9345005 10.1016/j.asoc.2020.106744 10.1016/j.ins.2020.09.041 10.1016/j.neucom.2022.01.055 10.1109/ICCV.2017.89 10.1109/CVPR.2018.00813 10.1109/TNNLS.2021.3114747 10.1109/TMI.2019.2893944 10.1109/ICCV.2017.324 10.1109/ACCESS.2020.3010287 10.1016/j.compbiomed.2021.104319 10.1109/WACV.2018.00097 10.1109/TCBB.2021.3065361 10.1007/978-3-319-23344-4_37 10.1007/978-3-319-23117-4_43

Multi-center validation of an artificial intelligence system for detection of COVID-19 on chest radiographs in symptomatic patients.

While chest radiograph (CXR) is the first-line imaging investigation in patients with respiratory symptoms, differentiating COVID-19 from other respiratory infections on CXR remains challenging. We developed and validated an AI system for COVID-19 detection on presenting CXR. A deep learning model (RadGenX), trained on 168,850 CXRs, was validated on a large international test set of presenting CXRs of symptomatic patients from 9 study sites (US, Italy, and Hong Kong SAR) and 2 public datasets from the US and Europe. Performance was measured by area under the receiver operator characteristic curve (AUC). Bootstrapped simulations were performed to assess performance across a range of potential COVID-19 disease prevalence values (3.33 to 33.3%). Comparison against international radiologists was performed on an independent test set of 852 cases. RadGenX achieved an AUC of 0.89 on 4-fold cross-validation and an AUC of 0.79 (95%CI 0.78-0.80) on an independent test cohort of 5,894 patients. Delong's test showed statistical differences in model performance across patients from different regions (p < 0.01), disease severity (p < 0.001), gender (p < 0.001), and age (p = 0.03). Prevalence simulations showed the negative predictive value increases from 86.1% at 33.3% prevalence, to greater than 98.5% at any prevalence below 4.5%. Compared with radiologists, McNemar's test showed the model has higher sensitivity (p < 0.001) but lower specificity (p < 0.001). An AI model that predicts COVID-19 infection on CXR in symptomatic patients was validated on a large international cohort providing valuable context on testing and performance expectations for AI systems that perform COVID-19 prediction on CXR. • An AI model developed using CXRs to detect COVID-19 was validated in a large multi-center cohort of 5,894 patients from 9 prospectively recruited sites and 2 public datasets. • Differences in AI model performance were seen across region, disease severity, gender, and age. • Prevalence simulations on the international test set demonstrate the model's NPV is greater than 98.5% at any prevalence below 4.5%.

European radiology

10.1007/s00330-022-08969-z 10.1016/S1473-3099(20)30457-6 10.1001/jamanetworkopen.2020.37067 10.1016/S2468-2667(20)30308-X 10.1056/NEJMp2025631 10.1148/radiol.2020200432 10.1148/radiol.2020201365 10.1038/s41598-020-76550-z 10.1148/radiol.2020203511 10.1109/TMI.2020.2993291 10.1016/j.patrec.2020.09.010 10.1155/2020/8889023 10.1109/JBHI.2020.3037127 10.1148/ryai.2020200029 10.1016/j.media.2021.102225 10.1148/radiol.2020200038 10.1038/s41597-020-00741-6 10.1038/s42256-021-00307-0 10.1371/journal.pmed.1002683 10.1038/s41467-020-19802-w 10.1016/S2213-2600(21)00005-9 10.1016/S2666-5247(20)30200-7 10.1126/sciadv.abd5393

Explainable deep learning algorithm for distinguishing incomplete Kawasaki disease by coronary artery lesions on echocardiographic imaging.

Incomplete Kawasaki disease (KD) has often been misdiagnosed due to a lack of the clinical manifestations of classic KD. However, it is associated with a markedly higher prevalence of coronary artery lesions. Identifying coronary artery lesions by echocardiography is important for the timely diagnosis of and favorable outcomes in KD. Moreover, similar to KD, coronavirus disease 2019, currently causing a worldwide pandemic, also manifests with fever; therefore, it is crucial at this moment that KD should be distinguished clearly among the febrile diseases in children. In this study, we aimed to validate a deep learning algorithm for classification of KD and other acute febrile diseases. We obtained coronary artery images by echocardiography of children (n = 138 for KD; n = 65 for pneumonia). We trained six deep learning networks (VGG19, Xception, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) using the collected data. SE-ResNext50 showed the best performance in terms of accuracy, specificity, and precision in the classification. SE-ResNext50 offered a precision of 81.12%, a sensitivity of 84.06%, and a specificity of 58.46%. The results of our study suggested that deep learning algorithms have similar performance to an experienced cardiologist in detecting coronary artery lesions to facilitate the diagnosis of KD.

Computer methods and programs in biomedicine

10.1016/j.cmpb.2022.106970

CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network.

The use of computer-assisted analysis to improve image interpretation has been a long-standing challenge in the medical imaging industry. In terms of image comprehension, Continuous advances in AI (Artificial Intelligence), predominantly in DL (Deep Learning) techniques, are supporting in the classification, Detection, and quantification of anomalies in medical images. DL techniques are the most rapidly evolving branch of AI, and it's recently been successfully pragmatic in a variety of fields, including medicine. This paper provides a classification method for COVID 19 infected X-ray images based on new novel deep CNN model. For COVID19 specified pneumonia analysis, two new customized CNN architectures, CVD-HNet1 (COVID-HybridNetwork1) and CVD-HNet2 (COVID-HybridNetwork2), have been designed. The suggested method utilizes operations based on boundaries and regions, as well as convolution processes, in a systematic manner. In comparison to existing CNNs, the suggested classification method achieves excellent Accuracy 98 percent, F Score 0.99 and MCC 0.97. These results indicate impressive classification accuracy on a limited dataset, with more training examples, much better results can be achieved. Overall, our CVD-HNet model could be a useful tool for radiologists in diagnosing and detecting COVID 19 instances early.

Wireless personal communications

10.1007/s11277-022-09864-y 10.1183/13993003.00775-2020 10.1038/s41598-020-76282-0 10.1109/ACCESS.2020.3005510 10.1007/s13246-020-00865-4 10.1007/s40846-020-00529-4 10.1016/j.cmpb.2020.105581 10.1109/TMI.2020.2993291 10.1016/j.cmpb.2020.105608 10.1016/j.pdpdt.2021.102473 10.1038/s42003-020-01535-7 10.1109/RBME.2020.2990959 10.1016/j.mri.2021.03.005 10.1016/j.clinimag.2020.09.002 10.1155/2021/6621607 10.1007/s10044-021-00970-4 10.2196/23693 10.1007/s12530-021-09385-2 10.1038/s42256-021-00307-0 10.1016/j.imu.2020.100427 10.1371/journal.pone.0250688 10.1007/s10140-020-01886-y 10.1088/2632-2153/abf22c 10.1371/journal.pone.0235187 10.1038/s41591-020-0931-3 10.1080/21681163.2015.1135299 10.1097/RLI.0000000000000341 10.1109/TMI.2015.2508280 10.1016/j.media.2016.05.004 10.4103/2153-3539.186902 10.1007/s11042-020-09981-5 10.1109/JBHI.2016.2631401 10.1007/978-3-319-0443-0_39 10.1007/s11277-021-09031-9.(IF-1.671) 10.1109/TMI.2016.2521800 10.1109/TMI.2016.2548501 10.1038/srep38897 10.3389/fnins.2014.00229 10.1007/s13735-021-00218-1 10.1007/s10278-016-9914-9 10.1109/JBHI.2016.2636665 10.1118/1.4967345 10.1002/9781119792253.ch8 10.1080/03772063.2021.1893231 10.1007/s11517-016-1590-x 10.3389/fonc.2020.01621

Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing.

The COVID-19 pandemic has changed the way we practice medicine. Cancer patient and obstetric care landscapes have been distorted. Delaying cancer diagnosis or maternal-fetal monitoring increased the number of preventable deaths or pregnancy complications. One solution is using Artificial Intelligence to help the medical personnel establish the diagnosis in a faster and more accurate manner. Deep learning is the state-of-the-art solution for image classification. Researchers manually design the structure of fix deep learning neural networks structures and afterwards verify their performance. The goal of this paper is to propose a potential method for learning deep network architectures automatically. As the number of networks architectures increases exponentially with the number of convolutional layers in the network, we propose a differential evolution algorithm to traverse the search space. At first, we propose a way to encode the network structure as a candidate solution of fixed-length integer array, followed by the initialization of differential evolution method. A set of random individuals is generated, followed by mutation, recombination, and selection. At each generation the individuals with the poorest loss values are eliminated and replaced with more competitive individuals. The model has been tested on three cancer datasets containing MRI scans and histopathological images and two maternal-fetal screening ultrasound images. The novel proposed method has been compared and statistically benchmarked to four state-of-the-art deep learning networks: VGG16, ResNet50, Inception V3, and DenseNet169. The experimental results showed that the model is competitive to other state-of-the-art models, obtaining accuracies between 78.73% and 99.50% depending on the dataset it had been applied on.

Computers in biology and medicine

10.1016/j.compbiomed.2022.105623 10.1159/000508254 10.3390/jcm9113749 10.1016/S2214-109X(21)00079-6 10.1111/jgh15325 10.1136/bmjpo-2020-000859 10.5281/zenodo.3904280 10.1002/uog.20945 10.1002/uog.20796 10.1109/ISBI.2019.8759377 10.1103/PhysRevE.101.052604 10.1038/s41467-021-26568-2 10.1146/annurev-conmatphys-031119-050745 10.1038/s42256-018-0006-z 10.1109/TAI.2021.3067574 10.34740/Kaggle/dsv/1183165 10.1080/00949655.2010.520163 10.3390/s21062222 10.1101/2020.08.15.20175760 10.10138/s41598-020-67076-5

Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0.

COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization. ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted. Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions. Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings.

Computers in biology and medicine

10.1016/j.compbiomed.2022.105571 10.1002/jmv.25996 10.1002/jmv.25855 10.23736/S0392-9590.21.04771-4

Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification.

Chest X-ray (CXR) is a non-invasive imaging modality used in the prognosis and management of chronic lung disorders like tuberculosis (TB), pneumonia, coronavirus disease (COVID-19), etc. The radiomic features associated with different disease manifestations assist in detection, localization, and grading the severity of infected lung regions. The majority of the existing computer-aided diagnosis (CAD) system used these features for the classification task, and only a few works have been dedicated to disease-localization and severity scoring. Moreover, the existing deep learning approaches use class activation map and Saliency map, which generate a rough localization. This study aims to generate a compact disease boundary, infection map, and grade the infection severity using proposed multistage superpixel classification-based disease localization and severity assessment framework. The proposed method uses a simple linear iterative clustering (SLIC) technique to subdivide the lung field into small superpixels. Initially, the different radiomic texture and proposed shape features are extracted and combined to train different benchmark classifiers in a multistage framework. Subsequently, the predicted class labels are used to generate an infection map, mark disease boundary, and grade the infection severity. The performance is evaluated using a publicly available Montgomery dataset and validated using Friedman average ranking and Holm and Nemenyi post-hoc procedures. The proposed multistage classification approach achieved accuracy (ACC)= 95.52%, F-Measure (FM)= 95.48%, area under the curve (AUC)= 0.955 for Stage-I and ACC=85.35%, FM=85.20%, AUC=0.853 for Stage-II using calibration dataset and ACC = 93.41%, FM = 95.32%, AUC = 0.936 for Stage-I and ACC = 84.02%, FM = 71.01%, AUC = 0.795 for Stage-II using validation dataset. Also, the model has demonstrated the average Jaccard Index (JI) of 0.82 and Pearson's correlation coefficient (r) of 0.9589. The obtained classification results using calibration and validation dataset confirms the promising performance of the proposed framework. Also, the average JI shows promising potential to localize the disease, and better agreement between radiologist score and predicted severity score (r) confirms the robustness of the method. Finally, the statistical test justified the significance of the obtained results.

Computer methods and programs in biomedicine

10.1016/j.cmpb.2022.106947 10.1016/j.media.2020.101847 10.1016/j.media.2020.101846 10.1016/j.eswa.2020.113514 10.1016/j.measurement.2019.05.076 10.1016/j.eswa.2020.113909 10.1016/j.media.2021.102046 10.1016/j.patcog.2020.107613 10.1016/j.media.2018.12.007 10.1007/s10916-019-1222-8 10.1109/ICPC2T48082.2020.9071445 10.1109/tmi.2020.2993291 10.1016/j.media.2021.101978 10.1016/j.media.2020.101911 10.1016/j.media.2015.09.004 10.1016/j.media.2020.101913 10.1109/ACCESS.2020.2971257 10.1109/ACCESS.2020.3003810 10.1109/ICCV.2017.74 10.1007/s13755-021-00146-8 10.1109/TPAMI.2012.120 10.3990/2.401 10.3390/s17071474 10.1186/s12880-019-0369-6 10.1016/j.media.2018.05.006 10.1109/CVPR.2017.369 10.1109/CVPR.2018.00865 10.1007/s10723-021-09596-6 10.1049/ipr2.12153 10.1109/LSC.2018.8572113 10.1109/JBHI.2021.3069169 10.1007/s13755-021-00146-8 10.3390/diagnostics12010025 10.1016/j.compbiomed.2021.105002 10.1016/j.media.2021.102299 10.1259/bjr.20210759 10.1016/j.media.2020.101860 10.1016/j.patcog.2021.107828 10.1016/j.media.2021.102054 10.1016/j.compbiomed.2022.105466 10.1016/j.scs.2021.103252 10.1007/s13369-021-05958-0 10.5114/pjr.2022.113435 10.3390/diagnostics11040616 10.1088/1742-6596/1767/1/012004 10.1109/TMI.2020.3040950 10.1109/JBHI.2020.3037127 10.1016/j.media.2020.101794 10.1038/s41598-019-42557-4 10.1109/TMI.2013.2290491 10.1109/TMI.2013.2284099 10.1016/j.measurement.2019.107426 10.1007/s00330-020-07504-2 10.1016/j.ijid.2020.05.021 10.1016/j.ijmedinf.2019.06.017 10.1109/TSMC.1973.4309314 10.1016/j.media.2020.101819 10.1016/j.advengsoft.2013.12.007 10.1007/s10462-009-9124-7 10.1016/B978-0-12-809633-8.20473-1 10.11613/BM.2014.003 10.1109/TMI.2017.2775636 10.5152/dir.2020.20205

Lung Sonography in Critical Care Medicine.

During the last five decades, lung sonography has developed into a core competency of intensive care medicine. It is a highly accurate bedside tool, with clear diagnostic criteria for most causes of respiratory failure (pneumothorax, pulmonary edema, pneumonia, pulmonary embolism, chronic obstructive pulmonary disease, asthma, and pleural effusion). It helps in distinguishing a hypovolemic from a cardiogenic, obstructive, or distributive shock. In addition to diagnostics, it can also be used to guide ventilator settings, fluid administration, and even antimicrobial therapy, as well as to assess diaphragmatic function. Moreover, it provides risk-reducing guidance during invasive procedures, e.g., intubation, thoracocentesis, or percutaneous dilatational tracheostomy. The recent pandemic has further increased its scope of clinical applications in the management of COVID-19 patients, from their initial presentation at the emergency department, during their hospitalization, and after their discharge into the community. Despite its increasing use, a consensus on education, assessment of competencies, and certification is still missing. Deep learning and artificial intelligence are constantly developing in medical imaging, and contrast-enhanced ultrasound enables new diagnostic perspectives. This review summarizes the clinical aspects of lung sonography in intensive care medicine and provides an overview about current training modalities, diagnostic limitations, and future developments.

Diagnostics (Basel, Switzerland)

10.3390/diagnostics12061405 10.1007/s00134-015-3952-5 10.1186/s13089-017-0059-y 10.1016/S2213-2600(14)70135-3 10.1378/chest.14-2608 10.1097/MD.0000000000005713 10.1186/cc13016 10.1590/S1516-31802010000200009 10.1097/CCM.0000000000003129 10.1097/CCM.0b013e31824e68ae 10.3760/CMA.J.ISSN.2095-4352.2015.07.008 10.1007/s00134-016-4411-7 10.1186/cc13859 10.1164/rccm.201003-0369OC 10.1056/NEJMra072149 10.7754/Clin.Lab.2017.170730 10.1213/ANE.0b013e3181e7cc42 10.1002/uog.22034 10.1016/j.ajog.2020.04.020 10.21106/ijma.294 10.1016/j.aca.2020.10.009 10.1016/j.ultrasmedbio.2020.04.033 10.3390/diagnostics11122202 10.1016/j.crad.2020.05.001 10.1007/s00134-021-06506-y 10.1378/chest.13-0882 10.1056/NEJMra0909487 10.1097/01.CCM.0000260624.99430.22 10.1378/chest.07-2800 10.1002/jum.15285 10.1016/j.acra.2020.07.002 10.1164/rccm.201802-0227LE 10.15557/JoU.2021.0036 10.1093/BJACEACCP/MKV012 10.1186/2110-5820-4-1 10.4103/0970-2113.156245 10.1097/00005373-200204000-00029 10.4103/1658-354X.197351 10.1164/ajrccm.156.5.96-07096 10.1007/s00134-012-2513-4 10.1007/s00134-003-1930-9 10.1007/s00134-010-2079-y 10.1016/j.ajem.2016.07.032 10.5811/westjem.2015.3.24809 10.1007/s001340000627 10.1016/j.redar.2020.02.008 10.1378/chest.108.5.1345 10.1197/j.aem.2005.05.005 10.1097/01.TA.0000133565.88871.E4 10.1007/s00134-014-3402-9 10.1378/chest.12-1445 10.1097/01.CCM.0000164542.86954.B4 10.1097/00005373-200102000-00003 10.1007/978-3-319-44072-9_4 10.1186/1472-6920-9-3 10.1111/j.1553-2712.2008.00347.x 10.1186/1476-7120-4-34 10.3389/fphys.2022.838479 10.1378/chest.09-0001 10.1186/1476-7120-6-16 10.7863/jum.2003.22.2.173 10.1378/chest.10-0435 10.1007/s001340050771 10.1159/000074195 10.1186/s12890-015-0091-2 10.1097/CCM.0b013e3181b08cdb 10.1007/s00134-017-4941-7 10.3390/JCM11051224 10.1097/CCM.0000000000003340 10.1186/cc5668 10.1097/CCM.0000000000003377 10.1007/978-3-642-37096-0_22 10.1378/chest.128.3.1531 10.1016/0301-5629(95)02003-9 10.1378/chest.13-1087 10.1378/chest.08-2281 10.1136/emj.2010.101584 10.1378/chest.12-0364 10.1016/j.chest.2015.12.012 10.3390/jcm10112453 10.2214/ajr.159.4.1529829 10.1007/s001340050988 10.1007/s00134-005-0024-2 10.1097/01.CCM.0000171532.02639.08 10.1378/chest.127.1.224 10.1007/s40477-017-0266-1 10.1148/radiology.191.3.8184046 10.1097/00000542-200401000-00006 10.2214/ajr.159.1.1609716 10.1097/00063198-200307000-00007 10.1002/jcu.1870190206 10.1136/thx.2008.100545 10.1378/chest.126.1.129 10.1148/rg.322115127 10.1016/j.ultrasmedbio.2010.10.004 10.1111/j.1440-1843.2011.02005.x 10.1097/CCM.0b013e3182266408 10.1177/0885066615583639 10.1186/2036-7902-6-8 10.1007/s00134-012-2547-7 10.1186/s13054-015-0894-9 10.1164/rccm.201004-0670OC 10.1164/rccm.201602-0367OC 10.1016/S0009-9260(05)82987-3 10.1007/s00134-015-4125-2 10.1136/thoraxjnl-2013-204111 10.1016/S0012-3692(15)32912-3 10.1097/00003246-199612000-00020 10.1007/s12630-014-0301-z 10.1016/j.annemergmed.2006.07.004 10.1197/j.aem.2005.08.014 10.1017/S1049023X00002004 10.1002/ppul.25955 10.1378/chest.125.3.1059 10.1186/1477-7819-12-139 10.1001/archinternmed.2009.548 10.1378/chest.12-0447 10.1378/chest.123.2.436 10.5402/2012/676524 10.1378/chest.11-0348 10.1177/0885066618755334 10.1007/s00408-019-00230-7 10.1002/jum.14448 10.1007/s12028-009-9259-z 10.1186/cc10344 10.2139/ssrn.3544750 10.26355/EURREV_202003_20549 10.1148/radiol.2020200847 10.1186/s13089-020-00171-w 10.4269/ajtmh.20-0280 10.1002/emp2.12194 10.1186/S13089-021-00250-6 10.1378/chest.08-2305 10.1007/S00134-011-2246-9 10.1016/j.chest.2019.08.859 10.1016/j.chest.2019.08.806 10.1186/s13089-018-0103-6 10.1186/cc10194 10.1007/s00134-009-1531-3 10.1186/s13089-017-0081-0 10.2214/ajr.168.2.9016251 10.2214/ajr.164.6.7754907 10.1109/TUFFC.2021.3094849 10.1109/TMI.2020.2994459 10.1109/JBHI.2019.2936151 10.1109/TUFFC.2020.3002249 10.1016/S0140-6736(20)31875-4 10.1055/A-0586-1107 10.1016/j.jus.2008.05.008 10.4329/wjr.v5.i10.372 10.1002/jum.15338 10.1007/s00134-020-06085-4

Novel COVID-19 Diagnosis Delivery App Using Computed Tomography Images Analyzed with Saliency-Preprocessing and Deep Learning.

This app project was aimed to remotely deliver diagnoses and disease-progression information to COVID-19 patients to help minimize risk during this and future pandemics. Data collected from chest computed tomography (CT) scans of COVID-19-infected patients were shared through the app. In this article, we focused on image preprocessing techniques to identify and highlight areas with ground glass opacity (GGO) and pulmonary infiltrates (PIs) in CT image sequences of COVID-19 cases. Convolutional neural networks (CNNs) were used to classify the disease progression of pneumonia. Each GGO and PI pattern was highlighted with saliency map fusion, and the resulting map was used to train and test a CNN classification scheme with three classes. In addition to patients, this information was shared between the respiratory triage/radiologist and the COVID-19 multidisciplinary teams with the application so that the severity of the disease could be understood through CT and medical diagnosis. The three-class, disease-level COVID-19 classification results exhibited a macro-precision of more than 94.89% in a two-fold cross-validation. Both the segmentation and classification results were comparable to those made by a medical specialist.

Tomography (Ann Arbor, Mich.)

10.3390/tomography8030134 10.1016/j.radi.2020.05.012 10.1148/radiol.2020201160 10.1007/s00330-020-06955-x 10.1148/radiol.2020200343 10.2214/AJR.20.22976 10.1016/j.diii.2020.03.014 10.1016/j.ejrad.2020.108941 10.5152/dir.2020.20144 10.1148/radiol.2020200230 10.1007/s00330-020-06976-6 10.1056/NEJMoa2002032 10.1111/jgh.15094 10.3390/diagnostics12040846 10.1016/j.ejrad.2020.109008 10.1016/j.jrid.2020.04.001 10.1109/TNNLS.2016.2582924 10.1021/acs.jcim.8b00706 10.1109/ACCESS.2020.3009328 10.48550/arXiv.2004.07407 10.1016/j.compbiomed.2020.104037 10.1016/j.patrec.2020.10.001 10.1148/radiol.2020200905 10.1109/ACCESS.2021.3058854 10.1109/TIP.2021.3058783 10.1007/BF00133570 10.1109/TIP.2008.925375 10.1155/2021/8869372 10.1007/s10140-021-01937-y 10.1109/TPAMI.1986.4767851 10.1117/1.JBO.23.5.056005 10.1148/ryct.2020200213 10.1097/RLI.0000000000000670 10.1148/radiol.2020200843 10.1148/radiol.2020201473 10.1109/5.726791 10.1145/3065386 10.1109/ic-ETITE47903.2020.049 10.1109/TEVC.2021.3088631 10.1136/bmj.m998 10.2196/18810 10.1109/42.363096

Convolutional neural network based CT scan classification method for COVID-19 test validation.

Given the novel corona virus discovered in Wuhan, China, in December 2019, due to the high false-negative rate of RT-PCR and the time-consuming to obtain the results, research has proved that computed tomography (CT) has become an auxiliary One of the essential means of diagnosis and treatment of new corona virus pneumonia. Since few COVID-19 CT datasets are currently available, it is proposed to use conditional generative adversarial networks to enhance data to obtain CT datasets with more samples to reduce the risk of over fitting. In addition, a BIN residual block-based method is proposed. The improved U-Net network is used for image segmentation and then combined with multi-layer perception for classification prediction. By comparing with network models such as AlexNet and GoogleNet, it is concluded that the proposed BUF-Net network model has the best performance, reaching an accuracy rate of 93%. Using Grad-CAM technology to visualize the system's output can more intuitively illustrate the critical role of CT images in diagnosing COVID-19. Applying deep learning using the proposed techniques suggested by the above study in medical imaging can help radiologists achieve more effective diagnoses that is the main objective of the research. On the basis of the foregoing, this study proposes to employ CGAN technology to augment the restricted data set, integrate the residual block into the U-Net network, and combine multi-layer perception in order to construct new network architecture for COVID-19 detection using CT images. -19. Given the scarcity of COVID-19 CT datasets, it is proposed that conditional generative adversarial networks be used to augment data in order to obtain CT datasets with more samples and therefore lower the danger of overfitting.

Smart health (Amsterdam, Netherlands)

10.1016/j.smhl.2022.100296 10.2174/1573405617666210215143503 10.1108/WJE-12-2020-0631 10.1109/ICAS49788.2021.9551169 10.1108/WJE-09-2020-0450 10.1109/JBHI.2021.3060035 10.1109/ASYU52992.2021.9598993 10.1109/ACCESS.2020.3005510 10.1109/JBHI.2020.3042523 10.1155/2021/9293877 10.1109/JBHI.2021.3051470 10.1109/ESCI50559.2021.9396773 10.1109/ISITIA52817.2021.9502217 10.1109/ICOIACT50329.2020.9332123 10.4018/IJSIR.287544 10.1109/TMI.2020.2994908 10.1109/CAC51589.2020.9326470 10.1109/TMI.2021.3104474 10.1109/TBDATA.2021.3056564 10.1109/JBHI.2021.3067465

Improved Analysis of COVID-19 Influenced Pneumonia from the Chest X-Rays Using Fine-Tuned Residual Networks.

COVID-19 has remained a threat to world life despite a recent reduction in cases. There is still a possibility that the virus will evolve and become more contagious. If such a situation occurs, the resulting calamity will be worse than in the past if we act irresponsibly. COVID-19 must be widely screened and recognized early to avert a global epidemic. Positive individuals should be quarantined immediately, as this is the only effective way to prevent a global tragedy that has occurred previously. No positive case should go unrecognized. However, current COVID-19 detection procedures require a significant amount of time during human examination based on genetic and imaging techniques. Apart from RT-PCR and antigen-based tests, CXR and CT imaging techniques aid in the rapid and cost-effective identification of COVID. However, discriminating between diseased and normal X-rays is a time-consuming and challenging task requiring an expert's skill. In such a case, the only solution was an automatic diagnosis strategy for identifying COVID-19 instances from chest X-ray images. This article utilized a deep convolutional neural network, ResNet, which has been demonstrated to be the most effective for image classification. The present model is trained using pretrained ResNet on ImageNet weights. The versions of ResNet34, ResNet50, and ResNet101 were implemented and validated against the dataset. With a more extensive network, the accuracy appeared to improve. Nonetheless, our objective was to balance accuracy and training time on a larger dataset. By comparing the prediction outcomes of the three models, we concluded that ResNet34 is a more likely candidate for COVID-19 detection from chest X-rays. The highest accuracy level reached 98.34%, which was higher than the accuracy achieved by other state-of-the-art approaches examined in earlier studies. Subsequent analysis indicated that the incorrect predictions occurred with approximately 100% certainty. This uncovered a severe weakness in CNN, particularly in the medical area, where critical decisions are made. However, this can be addressed further in a future study by developing a modified model to incorporate uncertainty into the predictions, allowing medical personnel to manually review the incorrect predictions.

Computational intelligence and neuroscience

10.1155/2022/9414567 10.1016/s0140-6736(20)30183-5 10.1001/jama.2020.3786 10.2807/1560-7917.ES.2020.25.3.2000045 10.3201/eid2606.200301 10.1038/s42256-021-00338-7 10.5114/pjr.2020.100788 10.1148/radiol.2020200432 10.1148/radiol.2020200343 10.1016/j.media.2020.101794 10.1146/annurev.bioeng.8.061505.095802 10.1093/bib/bbx044 10.1097/rli.0000000000000672 10.1016/j.bbe.2020.08.008 10.1016/j.media.2017.07.005 10.1148/radiol.2019192515 10.1109/access.2020.3016780 10.1109/tii.2021.3057524 10.1371/journal.pone.0255886 10.1109/cvpr.2017.369 10.1007/s13246-020-00865-4 10.1016/j.cell.2018.02.010 10.1016/j.compbiomed.2020.103792 10.1109/access.2020.3010287 10.1016/j.cmpb.2020.105581 10.1007/s12559-021-09848-3 10.1109/access.2020.2994762 10.1016/j.compbiomed.2021.104319 10.1038/s41598-020-76550-z 10.1155/2021/8828404 10.1109/iccc51575.2020.9344870 10.1155/2021/3281135 10.1117/1.jmi.8.s1.017503 10.1109/aset.2018.8379889 10.1016/j.knosys.2015.01.010 10.1007/s11263-015-0816-y 10.1109/CVPR.2016.90 10.1016/j.patcog.2019.01.006 10.1371/journal.pone.0118432 10.1007/978-981-15-5281-6_7 10.1016/j.mehy.2020.109761 10.1101/2020.07.08.20149161 10.1007/s10044-021-00970-4 10.1155/2021/6799202 10.1109/cvpr.2015.7298640

Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review.

Research on the diagnosis of COVID-19 using lung images is limited by the scarcity of imaging data. Generative adversarial networks (GANs) are popular for synthesis and data augmentation. GANs have been explored for data augmentation to enhance the performance of artificial intelligence (AI) methods for the diagnosis of COVID-19 within lung computed tomography (CT) and X-ray images. However, the role of GANs in overcoming data scarcity for COVID-19 is not well understood. This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes different GAN methods and lung imaging data sets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. A search was conducted on 5 databases, namely PubMed, IEEEXplore, Association for Computing Machinery (ACM) Digital Library, Scopus, and Google Scholar. The search was conducted from October 11-13, 2021. The search was conducted using intervention keywords, such as "generative adversarial networks" and "GANs," and application keywords, such as "COVID-19" and "coronavirus." The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews. Only those studies were included that reported GAN-based methods for analyzing chest X-ray images, chest CT images, and chest ultrasound images. Any studies that used deep learning methods but did not use GANs were excluded. No restrictions were imposed on the country of publication, study design, or outcomes. Only those studies that were in English and were published from 2020 to 2022 were included. No studies before 2020 were included. This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lung imaging data. Most of the studies (n=42, 74%) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and superresolution of lung images. The cycleGAN and the conditional GAN were the most commonly used architectures, used in 9 studies each. In addition, 29 (51%) studies used chest X-ray images, while 21 (37%) studies used CT images for the training of GANs. For the majority of the studies (n=47, 82%), the experiments were conducted and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only 2 (4%) studies. Studies have shown that GANs have great potential to address the data scarcity challenge for lung images in COVID-19. Data synthesized with GANs have been helpful to improve the training of the convolutional neural network (CNN) models trained for the diagnosis of COVID-19. In addition, GANs have also contributed to enhancing the CNNs' performance through the superresolution of the images and segmentation. This review also identified key limitations of the potential transformation of GAN-based methods in clinical applications.

JMIR medical informatics

10.2196/37365 10.1016/S1473-3099(20)30235-8 10.1016/S1473-3099(20)30235-8 10.1002/jmv.25786 10.2196/20756 10.3389/fmed.2021.704256 10.3389/fmed.2021.704256 10.1109/access.2020.3010287 10.1152/physiolgenomics.00029.2020 10.1109/tai.2020.3020521 10.1109/access.2020.3023495 10.1007/s10916-018-1072-9 10.1016/j.media.2019.101552 10.1109/iccv.2017.244 10.3389/fpubh.2020.00164 10.3389/fpubh.2020.00164 10.1002/acm2.13121 10.2196/27414 10.7326/m18-0850 10.1109/jbhi.2020.3042523 10.1007/s00259-020-04929-1 10.1038/s41598-021-87994-2 10.1038/s41598-021-87994-2 10.1007/s13246-020-00952-6 10.1016/j.media.2021.102159 10.1007/s12559-020-09785-7 10.1007/s00521-020-05437-x 10.1016/j.compbiomed.2020.104181 10.1002/mp.15044 10.1007/s10796-021-10144-6 10.1007/s12539-020-00403-6 10.1016/j.eswa.2021.115681 10.2147/idr.s296346 10.1007/s00521-020-05636-6 10.3390/diagnostics11050895 10.1007/s12559-021-09926-6 10.1016/j.bspc.2021.102901 10.1007/s42979-021-00795-2 10.1016/j.csbj.2021.02.016 10.1016/j.bspc.2021.103182 10.1109/conit51480.2021.9498272 10.1109/isbi48211.2021.9434159 10.1109/access.2020.2994762 10.1109/bibm49941.2020.9313466 10.1109/icassp39728.2021.9414031 10.1109/cec45853.2021.9504743 10.1109/prai53619.2021.9551043 10.1109/bigdata50022.2020.9377878 10.1109/pic50277.2020.9350813 10.1109/tem.2021.3103334 10.1109/jbhi.2021.3067465 10.1109/csci51800.2020.00160 10.1109/access.2020.3025010 10.1109/isbi48211.2021.9433806 10.3390/sym12040651 10.1007/s13278-021-00731-5 10.3390/engproc2021007006 10.1109/access.2020.3017915 10.1155/2021/6680455 10.32604/csse.2021.017191 10.3390/app11167174 10.1145/3458744.3474039 10.1145/3449639.3459319 10.1007/s00521-021-06344-5 10.1016/j.aej.2021.01.011 10.1016/j.eswa.2021.115401 10.1007/978-3-030-60802-6_36 10.3390/sym12091530 10.32604/cmc.2022.018547 10.32604/cmc.2022.018564 10.1007/s10489-020-01867-1 10.1007/978-3-030-86340-1_47 10.1007/978-3-030-68035-0_12 10.1117/12.2582162

Automated Multi-View Multi-Modal Assessment of COVID-19 Patients Using Reciprocal Attention and Biomedical Transform.

Automated severity assessment of coronavirus disease 2019 (COVID-19) patients can help rationally allocate medical resources and improve patients' survival rates. The existing methods conduct severity assessment tasks mainly on a unitary modal and single view, which is appropriate to exclude potential interactive information. To tackle the problem, in this paper, we propose a multi-view multi-modal model to automatically assess the severity of COVID-19 patients based on deep learning. The proposed model receives multi-view ultrasound images and biomedical indices of patients and generates comprehensive features for assessment tasks. Also, we propose a reciprocal attention module to acquire the underlying interactions between multi-view ultrasound data. Moreover, we propose biomedical transform module to integrate biomedical data with ultrasound data to produce multi-modal features. The proposed model is trained and tested on compound datasets, and it yields 92.75% for accuracy and 80.95% for recall, which is the best performance compared to other state-of-the-art methods. Further ablation experiments and discussions conformably indicate the feasibility and advancement of the proposed model.

Frontiers in public health

10.3389/fpubh.2022.886958 10.1056/NEJMoa2001017 10.1056/NEJMoa2001191 10.1148/radiol.2020200642 10.1148/ryct.2020200034 10.1016/j.compbiomed.2021.104721 10.1016/j.advms.2020.06.005 10.1007/s10439-015-1495-0 10.1016/S2213-2600(20)30120-X 10.1007/978-3-030-32245-8_64 10.1016/j.compmedimag.2019.101688 10.1007/s00330-019-06163-2 10.1001/jama.2016.17216 10.1001/jama.2017.18152 10.1016/j.cell.2018.02.010 10.1148/radiol.2020200432 10.1038/s42256-020-0180-7 10.1016/j.cell.2020.05.032 10.1038/s41467-020-17971-2 10.1038/s41467-020-17280-8 10.1038/s41598-020-76550-z 10.1038/s41598-020-76282-0 10.3390/diagnostics12010025 10.1016/j.compbiomed.2020.104037 10.1016/j.media.2021.102299 10.1016/j.bspc.2021.102622 10.1016/j.rinp.2021.104495 10.1038/s41598-022-05052-x 10.1145/3462635 10.1038/s41598-021-93543-8 10.1016/j.bspc.2021.103182 10.1002/jum.15285 10.48550/arXiv.1412.6980 10.1109/ICCV.2017.324 10.48550/arXiv.1409.1556 10.1109/CVPR.2016.90 10.1109/CVPR.2017.243 10.1109/CVPR.2018.00745 10.1109/CVPR.2017.195 10.1109/ICCV.2017.74

A Deep Learning Model for Diagnosing COVID-19 and Pneumonia through X-ray.

The new global pandemic caused by the 2019 novel coronavirus (COVID-19), novel coronavirus pneumonia, has spread rapidly around the world, causing enormous damage to daily life, public health security, and the global economy. Early detection and treatment of COVID-19 infected patients are critical to prevent the further spread of the epidemic. However, existing detection methods are unable to rapidly detect COVID-19 patients, so infected individuals are not detected in a timely manner, which complicates the prevention and control of COVID-19 to some extent. Therefore, it is crucial to develop a rapid and practical COVID-19 detection method. In this work, we explored the application of deep learning in COVID-19 detection to develop a rapid COVID-19 detection method. Existing studies have shown that novel coronavirus pneumonia has significant radiographic performance. In this study, we analyze and select the features of chest radiographs. We propose a chest X-Ray (CXR) classification method based on the selected features and investigate the application of transfer learning in detecting pneumonia and COVID-19. Furthermore, we combine the proposed CXR classification method based on selected features with transfer learning and ensemble learning and propose an ensemble deep learning model based on transfer learning called COVID-ensemble to diagnose pneumonia and COVID-19 using chest x-ray images. The model aims to provide an accurate diagnosis for binary classification (no finding/pneumonia) and multivariate classification (COVID-19/No findings/ Pneumonia). Our proposed CXR classification method based on selection features can significantly improve the CXR classification accuracy of the CNN model. Using this method, DarkNet19 improved its binary and triple classification accuracies by 3.5% and 5.78%, respectively. In addition, the COVIDensemble achieved 91.5% accuracy in the binary classification task and 91.11% in the multi-category classification task. The experimental results demonstrate that the COVID-ensemble can quickly and accurately detect COVID-19 and pneumonia automatically through X-ray images and that the performance of this model is superior to that of several existing methods. Our proposed COVID-ensemble can not only overcome the limitations of the conventional COVID-19 detection method RT-PCR and provide convenient and fast COVID-19 detection but also automatically detect pneumonia, thereby reducing the pressure on the medical staff. Using deep learning models to automatically diagnose COVID-19 and pneumonia from X-ray images can serve as a fast and efficient screening method for COVID-19 and pneumonia.

Current medical imaging

10.2174/1573405618666220610093740

The Pitfalls of Using Open Data to Develop Deep Learning Solutions for COVID-19 Detection in Chest X-Rays.

Since the emergence of COVID-19, deep learning models have been developed to identify COVID-19 from chest X-rays. With little to no direct access to hospital data, the AI community relies heavily on public data comprising numerous data sources. Model performance results have been exceptional when training and testing on open-source data, surpassing the reported capabilities of AI in pneumonia-detection prior to the COVID-19 outbreak. In this study impactful models are trained on a widely used open-source data and tested on an external test set and a hospital dataset, for the task of classifying chest X-rays into one of three classes: COVID-19, non-COVID pneumonia and no-pneumonia. Classification performance of the models investigated is evaluated through ROC curves, confusion matrices and standard classification metrics. Explainability modules are implemented to explore the image features most important to classification. Data analysis and model evalutions show that the popular open-source dataset COVIDx is not representative of the real clinical problem and that results from testing on this are inflated. Dependence on open-source data can leave models vulnerable to bias and confounding variables, requiring careful analysis to develop clinically useful/viable AI tools for COVID-19 detection in chest X-rays.

Studies in health technology and informatics

10.3233/SHTI220164

Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission. Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann-Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set. No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set. By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted.

BMC medical imaging

10.1186/s12880-022-00833-2 10.1371/journal.pone.0245272 10.1111/joim.13091 10.4266/acc.2020.00381 10.1371/journal.pone.0243709 10.1093/cid/ciaa443 10.1001/jamainternmed.2020.2033 10.1093/cid/ciaa414 10.1186/s40560-021-00527-x 10.7150/ijms.48281 10.5114/pjr.2020.98009 10.7150/thno.46465 10.1148/radiol.2015151169 10.1038/s41598-021-83237-6 10.1371/journal.pone.0246582 10.1038/s41598-021-90991-0 10.7150/thno.46428 10.7150/ijbs.58855 10.1186/s12880-020-00529-5 10.1016/s0895-4356(96)00236-3 10.1186/1471-2105-14-106 10.2307/2531595 10.1016/j.ijid.2021.12.357 10.1016/S2589-7500(21)00039-X 10.1371/journal.pone.0230548 10.4081/jphr.2021.2270 10.2214/AJR.20.24044 10.2214/AJR.20.22976 10.1148/ryct.2020200322 10.1007/s11604-020-00956-y 10.1016/j.ijid.2020.10.095 10.5152/dir.2020.20451 10.1016/j.ejrad.2021.109552 10.1007/s11547-020-01197-9 10.1148/radiol.2021204522

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT.

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score.

Scientific reports

10.1038/s41598-022-13298-8 10.1001/jama.2020.1585 10.1016/S0140-6736(20)30566-3 10.1136/thoraxjnl-2020-216001 10.2214/AJR.20.22976 10.1148/radiol.2020201754 10.1148/ryai.2020200098 10.1016/j.media.2020.101860 10.1038/s41598-021-90991-0 10.1148/radiol.2020200905 10.1038/s41746-021-00446-z 10.1038/s41598-021-84561-7 10.1148/RADIOL.2020202439 10.1038/s41467-020-17971-2 10.1016/j.cell.2020.04.045 10.1038/s41598-019-56989-5 10.1038/s41746-020-00369-1 10.1148/radiol.2020200370 10.1148/rg.2020200159 10.21037/atm-20-3026 10.1148/ryct.2020200322 10.1016/j.ejrad.2021.109583 10.1007/s00330-021-07957-z 10.1186/s41747-020-00173-2 10.1148/ryct.2020200110 10.1016/j.acra.2011.01.011 10.1109/TMI.2016.2535865 10.1016/j.acra.2015.12.021 10.1038/s41592-020-01008-z 10.21227/w3aw-rv39

Towards an effective model for lung disease classification: Using Dense Capsule Nets for early classification of lung diseases.

Machine Learning and computer vision have been the frontiers of the war against the COVID-19 Pandemic. Radiology has vastly improved the diagnosis of diseases, especially lung diseases, through the early assessment of key disease factors. Chest X-rays have thus become among the commonly used radiological tests to detect and diagnose many lung diseases. However, the discovery of lung disease through X-rays is a significantly challenging task depending on the availability of skilled radiologists. There has been a recent increase in attention to the design of Convolution Neural Networks (CNN) models for lung disease classification. A considerable amount of training dataset is required for CNN to work, but the problem is that it cannot handle translation and rotation correctly as input. The recently proposed Capsule Networks (referred to as CapsNets) are new automated learning architecture that aims to overcome the shortcomings in CNN. CapsNets are vital for rotation and complex translation. They require much less training information, which applies to the processing of data sets from medical images, including radiological images of the chest X-rays. In this research, the adoption and integration of CapsNets into the problem of chest X-ray classification have been explored. The aim is to design a deep model using CapsNet that increases the accuracy of the classification problem involved. We have used convolution blocks that take input images and generate convolution layers used as input to capsule block. There are 12 capsule layers operated, and the output of each capsule is used as an input to the next convolution block. The process is repeated for all blocks. The experimental results show that the proposed architecture yields better results when compared with the existing CNN techniques by achieving a better area under the curve (AUC) average. Furthermore, DNet checks the best performance in the ChestXray-14 data set on traditional CNN, and it is validated that DNet performs better with a higher level of total depth.

Applied soft computing

10.1016/j.asoc.2022.109077

Prior-aware autoencoders for lung pathology segmentation.

Segmentation of lung pathology in Computed Tomography (CT) images is of great importance for lung disease screening. However, the presence of different types of lung pathologies with a wide range of heterogeneities in size, shape, location, and texture, on one side, and their visual similarity with respect to surrounding tissues, on the other side, make it challenging to perform reliable automatic lesion segmentation. To leverage segmentation performance, we propose a deep learning framework comprising a Normal Appearance Autoencoder (NAA) model to learn the distribution of healthy lung regions and reconstruct pathology-free images from the corresponding pathological inputs by replacing the pathological regions with the characteristics of healthy tissues. Detected regions that represent prior information regarding the shape and location of pathologies are then integrated into a segmentation network to guide the attention of the model into more meaningful delineations. The proposed pipeline was tested on three types of lung pathologies, including pulmonary nodules, Non-Small Cell Lung Cancer (NSCLC), and Covid-19 lesion on five comprehensive datasets. The results show the superiority of the proposed prior model, which outperformed the baseline segmentation models in all the cases with significant margins. On average, adding the prior model improved the Dice coefficient for the segmentation of lung nodules by 0.038, NSCLCs by 0.101, and Covid-19 lesions by 0.041. We conclude that the proposed NAA model produces reliable prior knowledge regarding the lung pathologies, and integrating such knowledge into a prior segmentation network leads to more accurate delineations.

Medical image analysis

10.1016/j.media.2022.102491