pt-sk/ml_research_abs huggingface dataset:
Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases.
Authors: Caio B SMaior, João M MSantana, Isis DLins, Márcio J CMoura
Journal: PloS one

As SARS-CoV-2 has spread quickly throughout the world, the scientific community has spent major efforts on better understanding the characteristics of the virus and possible means to prevent, diagnose, and treat COVID-19. A valid approach presented in the literature is to develop an image-based method to support COVID-19 diagnosis using convolutional neural networks (CNN). Because the availability of radiological data is rather limited due to the novelty of COVID-19, several methodologies consider reduced datasets, which may be inadequate, biasing the model. Here, we performed an analysis combining six different databases using chest X-ray images from open datasets to distinguish images of infected patients while differentiating COVID-19 and pneumonia from 'no-findings' images. In addition, the performance of models created from fewer databases, which may imperceptibly overestimate their results, is discussed. Two CNN-based architectures were created to process images of different sizes (512 × 512, 768 × 768, 1024 × 1024, and 1536 × 1536). Our best model achieved a balanced accuracy (BA) of 87.7% in predicting one of the three classes ('no-findings', 'COVID-19', and 'pneumonia') and a specific balanced precision of 97.0% for 'COVID-19' class. We also provided binary classification with a precision of 91.0% for detection of sick patients (i.e., with COVID-19 or pneumonia) and 98.4% for COVID-19 detection (i.e., differentiating from 'no-findings' or 'pneumonia'). Indeed, despite we achieved an unrealistic 97.2% BA performance for one specific case, the proposed methodology of using multiple databases achieved better and less inflated results than from models with specific image datasets for training. Thus, this framework is promising for a low-cost, fast, and noninvasive means to support the diagnosis of COVID-19.
Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning.
Authors: FudanZheng, LiangLi, XiangZhang, YingSong, ZiwangHuang, YutianChong, ZhiguangChen, HuilingZhu, JiahaoWu, WeifengChen, YutongLu, YuedongYang, YunfeiZha, HuiyingZhao, JunShen
Journal: Interdisciplinary sciences, computational life sciences

Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic CT image diagnosis system to assist doctors in diagnosis. Previous studies devoted to COVID-19 in the past months focused mostly on discriminating COVID-19 infected patients from healthy persons and/or bacterial pneumonia patients, and have ignored typical viral pneumonia since it is hard to collect samples for viral pneumonia that is less frequent in adults. In addition, it is much more challenging to discriminate COVID-19 from typical viral pneumonia as COVID-19 is also a kind of virus. In this study, we have collected CT images of 262, 100, 219, and 78 persons for COVID-19, bacterial pneumonia, typical viral pneumonia, and healthy controls, respectively. To the best of our knowledge, this was the first study of quaternary classification to include also typical viral pneumonia. To effectively capture the subtle differences in CT images, we have constructed a new model by combining the ResNet50 backbone with SE blocks that was recently developed for fine image analysis. Our model was shown to outperform commonly used baseline models, achieving an overall accuracy of 0.94 with AUC of 0.96, recall of 0.94, precision of 0.95, and F1-score of 0.94. The model is available in https://github.com/Zhengfudan/COVID-19-Diagnosis-and-Pneumonia-Classification .
Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome.
Authors: FatemehHomayounieh, Marcio AloisioBezerra Cavalcanti Rockenbach, ShadiEbrahimian, RuhaniDoda Khera, Bernardo CBizzo, VarunBuch, RosaBabaei, HadiKarimi Mobin, ImanMohseni, MatthiasMitschke, MathisZimmermann, FelixDurlak, FranziskaRauch, Subba RDigumarthy, Mannudeep KKalra
Journal: Journal of digital imaging

To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 105 females; 136 males) with RT-PCR-positive COVID-19 pneumonia who underwent non-contrast chest CT at one of the two tertiary care hospitals (site A: Massachusetts General Hospital, USA; site B: Firoozgar Hospital Iran). We recorded patient age, gender, comorbid conditions, laboratory values, intensive care unit (ICU) admission, mechanical ventilation, and final outcome (recovery or death). Two thoracic radiologists reviewed all chest CTs to record type, extent of pulmonary opacities based on the percentage of lobe involved, and severity of respiratory motion artifacts. Thin-section CT images were processed with the prototype (Siemens Healthineers) to obtain quantitative features including lung volumes, volume and percentage of all-type and high-attenuation opacities (≥ -200 HU), and mean HU and standard deviation of opacities within a given lung region. These values are estimated for the total combined lung volume, and separately for each lung and each lung lobe. Multivariable analyses of variance (MANOVA) and multiple logistic regression were performed for data analyses. About 26% of chest CTs (62/241) had moderate to severe motion artifacts. There were no significant differences in the AUCs of quantitative features for predicting disease severity with and without motion artifacts (AUC 0.94-0.97) as well as for predicting patient outcome (AUC 0.7-0.77) (p > 0.5). Combination of the volume of all-attenuation opacities and the percentage of high-attenuation opacities (AUC 0.76-0.82, 95% confidence interval (CI) 0.73-0.82) had higher AUC for predicting ICU admission than the subjective severity scores (AUC 0.69-0.77, 95% CI 0.69-0.81). Despite a high frequency of motion artifacts, quantitative features of pulmonary opacities from chest CT can help differentiate patients with favorable and adverse outcomes.
Monitoring social distancing under various low light conditions with deep learning and a single motionless time of flight camera.
Authors: AdinaRahim, AyeshaMaqbool, TauseefRana
Journal: PloS one

The purpose of this work is to provide an effective social distance monitoring solution in low light environments in a pandemic situation. The raging coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has brought a global crisis with its deadly spread all over the world. In the absence of an effective treatment and vaccine the efforts to control this pandemic strictly rely on personal preventive actions, e.g., handwashing, face mask usage, environmental cleaning, and most importantly on social distancing which is the only expedient approach to cope with this situation. Low light environments can become a problem in the spread of disease because of people's night gatherings. Especially, in summers when the global temperature is at its peak, the situation can become more critical. Mostly, in cities where people have congested homes and no proper air cross-system is available. So, they find ways to get out of their homes with their families during the night to take fresh air. In such a situation, it is necessary to take effective measures to monitor the safety distance criteria to avoid more positive cases and to control the death toll. In this paper, a deep learning-based solution is proposed for the above-stated problem. The proposed framework utilizes the you only look once v4 (YOLO v4) model for real-time object detection and the social distance measuring approach is introduced with a single motionless time of flight (ToF) camera. The risk factor is indicated based on the calculated distance and safety distance violations are highlighted. Experimental results show that the proposed model exhibits good performance with 97.84% mean average precision (mAP) score and the observed mean absolute error (MAE) between actual and measured social distance values is 1.01 cm.
A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19).
Authors: ShuaiWang, BoKang, JinluMa, XianjunZeng, MingmingXiao, JiaGuo, MengjiaoCai, JingyiYang, YaodongLi, XiangfeiMeng, BoXu
Journal: European radiology

The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control.
We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation.
The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%.
These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis.
• The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.
SOM-LWL method for identification of COVID-19 on chest X-rays.
Authors: Ahmed HamzaOsman, Hani MoetqueAljahdali, Sultan MenwerAltarrazi, AliAhmed
Journal: PloS one

The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavirus disease (COVID-19) will help to limit its dissemination worldwide. Many initial studies have focused on the identification of the genetic material of coronavirus and have a poor detection rate for long-term surgery. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 in patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays. Other current studies have used non-public datasets and complex artificial intelligence (AI) systems. In our research, we suggested a new COVID-19 identification technique based on the locality-weighted learning and self-organization map (LWL-SOM) strategy for detecting and capturing COVID-19 cases. We first grouped images from chest X-ray datasets based on their similar features in different clusters using the SOM strategy in order to discriminate between the COVID-19 and non-COVID-19 cases. Then, we built our intelligent learning model based on the LWL algorithm to diagnose and detect COVID-19 cases. The proposed SOM-LWL model improved the correlation coefficient performance results between the Covid19, no-finding, and pneumonia cases; pneumonia and no-finding cases; Covid19 and pneumonia cases; and Covid19 and no-finding cases from 0.9613 to 0.9788, 0.6113 to 1 0.8783 to 0.9999, and 0.8894 to 1, respectively. The proposed LWL-SOM had better results for discriminating COVID-19 and non-COVID-19 patients than the current machine learning-based solutions using AI evaluation measures.
CoVNet-19: A Deep Learning model for the detection and analysis of COVID-19 patients.
Authors: PriyanshKedia, NoneAnjum, RahulKatarya
Journal: Applied soft computing

The ongoing fight with Novel Corona Virus, getting quick treatment, and rapid diagnosis reports have become an act of high priority. With millions getting infected daily and a fatality rate of 2%, we made it our motive to contribute a little to solve this real-world problem by accomplishing a significant and substantial method for diagnosing COVID-19 patients.
The Exponential growth of COVID-19 cases worldwide has severely affected the health care system of highly populated countries due to proportionally a smaller number of medical practitioners, testing kits, and other resources, thus becoming essential to identify the infected people. Catering to the above problems, the purpose of this paper is to formulate an accurate, efficient, and time-saving method for detecting positive corona patients.
In this paper, an Ensemble Deep Convolution Neural Network model "CoVNet-19" is being proposed that can unveil important diagnostic characteristics to find COVID-19 infected patients using X-ray images chest and help radiologists and medical experts to fight this pandemic.
The experimental results clearly show that the overall classification accuracy obtained with the proposed approach for three-class classification among COVID-19, Pneumonia, and Normal is 98.28%, along with an average precision and Recall of 98.33% and 98.33%, respectively. Besides this, for binary classification between Non-COVID and COVID Chest X-ray images, an overall accuracy of 99.71% was obtained.
Having a high diagnostic accuracy, our proposed ensemble Deep Learning classification model can be a productive and substantial contribution to detecting COVID-19 infected patients.
Codeless Deep Learning of COVID-19 Chest X-Ray Image Dataset with KNIME Analytics Platform.
Authors: Jun YoungAn, HoseokSeo, Young-GonKim, Kyu EunLee, SungwanKim, Hyoun-JoongKong
Journal: Healthcare informatics research

This paper proposes a method for computer-assisted diagnosis of coronavirus disease 2019 (COVID-19) through chest X-ray imaging using a deep learning model without writing a single line of code using the Konstanz Information Miner (KNIME) analytics platform.
We obtained 155 samples of posteroanterior chest X-ray images from COVID-19 open dataset repositories to develop a classification model using a simple convolutional neural network (CNN). All of the images contained diagnostic information for COVID-19 and other diseases. The model would classify whether a patient was infected with COVID-19 or not. Eighty percent of the images were used for model training, and the rest were used for testing. The graphic user interface-based programming in the KNIME enabled class label annotation, data preprocessing, CNN model training and testing, performance evaluation, and so on.
1,000 epochs training were performed to test the simple CNN model. The lower and upper bounds of positive predictive value (precision), sensitivity (recall), specificity, and f-measure are 92.3% and 94.4%. Both bounds of the model's accuracies were equal to 93.5% and 96.6% of the area under the receiver operating characteristic curve for the test set.
In this study, a researcher who does not have basic knowledge of python programming successfully performed deep learning analysis of chest x-ray image dataset using the KNIME independently. The KNIME will reduce the time spent and lower the threshold for deep learning research applied to healthcare.
Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph.
Authors: WeiqiuJin, ShuqinDong, ChangziDong, XiaodanYe
Journal: Computers in biology and medicine

Chest X-ray radiography (CXR) has been widely considered as an accessible, feasible, and convenient method to evaluate suspected patients' lung involvement during the COVID-19 pandemic. However, with the escalating number of suspected cases, traditional diagnosis via CXR fails to deliver results within a short period of time. Therefore, it is crucial to employ artificial intelligence (AI) to enhance CXRs for obtaining quick and accurate diagnoses. Previous studies have reported the feasibility of utilizing deep learning methods to screen for COVID-19 using CXR and CT results. However, these models only use a single deep learning network for chest radiograph detection; the accuracy of this approach required further improvement.
In this study, we propose a three-step hybrid ensemble model, including a feature extractor, a feature selector, and a classifier. First, a pre-trained AlexNet with an improved structure extracts the original image features. Then, the ReliefF algorithm is adopted to sort the extracted features, and a trial-and-error approach is used to select the n most important features to reduce the feature dimension. Finally, an SVM classifier provides classification results based on the n selected features.
Compared to five existing models (InceptionV3: 97.916 ± 0.408%; SqueezeNet: 97.189 ± 0.526%; VGG19: 96.520 ± 1.220%; ResNet50: 97.476 ± 0.513%; ResNet101: 98.241 ± 0.209%), the proposed model demonstrated the best performance in terms of overall accuracy rate (98.642 ± 0.398%). Additionally, compared to the existing models, the proposed model demonstrates a considerable improvement in classification time efficiency (SqueezeNet: 6.602 ± 0.001s; InceptionV3: 12.376 ± 0.002s; ResNet50: 10.952 ± 0.001s; ResNet101: 18.040 ± 0.002s; VGG19: 16.632 ± 0.002s; proposed model: 5.917 ± 0.001s).
The model proposed in this article is practical and effective, and can provide high-precision COVID-19 CXR detection. We demonstrated its suitability to aid medical professionals in distinguishing normal CXRs, viral pneumonia CXRs and COVID-19 CXRs efficiently on small sample sizes.
CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images.
Authors: TaherehJavaheri, MortezaHomayounfar, ZohrehAmoozgar, RezaReiazi, FatemehHomayounieh, EngyAbbas, AzadehLaali, Amir RezaRadmard, Mohammad HadiGharib, Seyed Ali JavadMousavi, OmidGhaemi, RosaBabaei, Hadi KarimiMobin, MehdiHosseinzadeh, RanaJahanban-Esfahlan, KhaledSeidi, Mannudeep KKalra, GuanglanZhang, L TChitkushev, BenjaminHaibe-Kains, RezaMalekzadeh, RezaRawassizadeh
Journal: NPJ digital medicine

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.
Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.
Authors: BohanLiu, PanLiu, LutaoDai, YanlinYang, PengXie, YiqingTan, JichengDu, WeiShan, ChenghuiZhao, QinZhong, XixiangLin, XizhouGuan, NingXing, YuhuiSun, WenjunWang, ZhibingZhang, XiaFu, YanqingFan, MeifangLi, NaZhang, LinLi, YaouLiu, LinXu, JingboDu, ZhenhuaZhao, XuelongHu, WeipengFan, RongpinWang, ChongchongWu, YongkangNie, LiuquanCheng, LinMa, ZongrenLi, QianJia, MinchaoLiu, HuayuanGuo, GaoHuang, HaipengShen, LiangZhang, PeifangZhang, GangGuo, HaoLi, WeiminAn, JianxinZhou, KunlunHe
Journal: Scientific reports

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.