{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T03:05:51Z","timestamp":1789959951440,"version":"4.0.1"},"reference-count":64,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T00:00:00Z","timestamp":1642723200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>The COVID-19 epidemic has a catastrophic impact on global well-being and public health. More than 27 million confirmed cases have been reported worldwide until now. Due to the growing number of confirmed cases, and challenges to the variations of the COVID-19, timely and accurate classification of healthy and infected patients is essential to control and treat COVID-19. We aim to develop a deep learning-based system for the persuasive classification and reliable detection of COVID-19 using chest radiography. Firstly, we evaluate the performance of various state-of-the-art convolutional neural networks (CNNs) proposed over recent years for medical image classification. Secondly, we develop and train CNN from scratch. In both cases, we use a public X-Ray dataset for training and validation purposes. For transfer learning, we obtain 100% accuracy for binary classification (i.e., Normal\/COVID-19) and 87.50% accuracy for tertiary classification (Normal\/COVID-19\/Pneumonia). With the CNN trained from scratch, we achieve 93.75% accuracy for tertiary classification. In the case of transfer learning, the classification accuracy drops with the increased number of classes. The results are demonstrated by comprehensive receiver operating characteristics (ROC) and confusion metric analysis with 10-fold cross-validation.<\/jats:p>","DOI":"10.1371\/journal.pone.0262052","type":"journal-article","created":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T14:02:20Z","timestamp":1642773740000},"page":"e0262052","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":72,"title":["Automated detection of COVID-19 through convolutional neural network using chest x-ray images"],"prefix":"10.1371","volume":"17","author":[{"given":"Rubina","family":"Sarki","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khandakar","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8465-0996","authenticated-orcid":true,"given":"Hua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanchun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kate","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,1,21]]},"reference":[{"key":"pone.0262052.ref001","doi-asserted-by":"crossref","DOI":"10.1001\/jama.2020.3786","article-title":"Detection of SARS-CoV-2 in different types of clinical specimens","author":"W Wang","year":"2020","journal-title":"Jama"},{"key":"pone.0262052.ref002","first-page":"200642","article-title":"Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases","author":"T Ai","year":"2020","journal-title":"Radiology"},{"key":"pone.0262052.ref003","doi-asserted-by":"crossref","DOI":"10.1007\/s13755-021-00152-w","article-title":"New bag of deep visual words based features to classify chest x-ray images for COVID-19 diagnosis","volume":"9","author":"C Sitaula","year":"2021","journal-title":"Health Information Science and Systems"},{"key":"pone.0262052.ref004","article-title":"Monitoring COVID-19 Pandemic through the Lens of Social Media using Natural Language Processing and Machine Learning","volume":"9","author":"Y Liu","year":"2021","journal-title":"Health Information Science and Systems"},{"issue":"18","key":"pone.0262052.ref005","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1056\/NEJMoa2002032","article-title":"Clinical characteristics of coronavirus disease 2019 in China","volume":"382","author":"Wj Guan","year":"2020","journal-title":"New England journal of medicine"},{"issue":"1","key":"pone.0262052.ref006","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1183\/09031936.01.00213501","article-title":"Imaging of pneumonia: trends and algorithms","volume":"18","author":"T Franquet","year":"2001","journal-title":"European Respiratory Journal"},{"key":"pone.0262052.ref007","first-page":"353","article-title":"Standardized interpretation of paediatric chest radiographs for the diagnosis of pneumonia in epidemiological studies","volume":"83","author":"T Cherian","year":"2005","journal-title":"Bulletin of the World Health Organization"},{"key":"pone.0262052.ref008","first-page":"1","article-title":"Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks","author":"A Narin","year":"2021","journal-title":"Pattern Analysis and Applications"},{"issue":"1","key":"pone.0262052.ref009","first-page":"1","article-title":"Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images","volume":"10","author":"L Wang","year":"2020","journal-title":"Scientific Reports"},{"key":"pone.0262052.ref010","doi-asserted-by":"crossref","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","article-title":"Automated detection of COVID-19 cases using deep neural networks with X-ray images","author":"T Ozturk","year":"2020","journal-title":"Computers in Biology and Medicine"},{"issue":"1","key":"pone.0262052.ref011","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13755-020-00129-1","article-title":"Automated epilepsy detection techniques from electroencephalogram signals: a review study","volume":"8","author":"S Supriya","year":"2020","journal-title":"Health Information Science and Systems"},{"issue":"7553","key":"pone.0262052.ref012","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Y LeCun","year":"2015","journal-title":"nature"},{"issue":"1","key":"pone.0262052.ref013","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13755-020-00125-5","article-title":"Automated detection of mild and multi-class diabetic eye diseases using deep learning","volume":"8","author":"R Sarki","year":"2020","journal-title":"Health Information Science and Systems"},{"issue":"1","key":"pone.0262052.ref014","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13755-018-0046-0","article-title":"Depression detection from social network data using machine learning techniques","volume":"6","author":"MR Islam","year":"2018","journal-title":"Health information science and systems"},{"issue":"1","key":"pone.0262052.ref015","first-page":"1","article-title":"Neural attention with character embeddings for hay fever detection from twitter","volume":"7","author":"J Du","year":"2019","journal-title":"Health information science and systems"},{"key":"pone.0262052.ref016","article-title":"Convolutional neural networks for mild diabetic retinopathy detection: an experimental study","author":"R Sarki","year":"2019","journal-title":"bioRxiv"},{"key":"pone.0262052.ref017","doi-asserted-by":"crossref","first-page":"151133","DOI":"10.1109\/ACCESS.2020.3015258","article-title":"Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey","volume":"8","author":"R Sarki","year":"2020","journal-title":"IEEE Access"},{"issue":"10","key":"pone.0262052.ref018","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.eng.2020.04.010","article-title":"A deep learning system to screen novel coronavirus disease 2019 pneumonia","volume":"6","author":"X Xu","year":"2020","journal-title":"Engineering"},{"key":"pone.0262052.ref019","unstructured":"Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, et al. Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv preprint arXiv:200305037. 2020;."},{"key":"pone.0262052.ref020","first-page":"200905","article-title":"Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT","author":"L Li","year":"2020","journal-title":"Radiology"},{"key":"pone.0262052.ref021","doi-asserted-by":"crossref","unstructured":"Shi F, Xia L, Shan F, Wu D, Wei Y, Yuan H, et al. Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification. arXiv preprint arXiv:200309860. 2020;.","DOI":"10.1088\/1361-6560\/abe838"},{"key":"pone.0262052.ref022","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi SA. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV). IEEE; 2016. p. 565\u2013571.","DOI":"10.1109\/3DV.2016.79"},{"issue":"6","key":"pone.0262052.ref023","doi-asserted-by":"crossref","first-page":"4082","DOI":"10.1007\/s10489-020-02051-1","article-title":"A deep learning system that generates quantitative CT reports for diagnosing pulmonary tuberculosis","volume":"51","author":"X Li","year":"2021","journal-title":"Applied Intelligence"},{"key":"pone.0262052.ref024","unstructured":"Shan F, Gao Y, Wang J, Shi W, Shi N, Han M, et al. Lung infection quantification of covid-19 in ct images with deep learning. arXiv preprint arXiv:200304655. 2020;."},{"issue":"1","key":"pone.0262052.ref025","first-page":"e200034","article-title":"Imaging profile of the COVID-19 infection: radiologic findings and literature review","volume":"2","author":"MY Ng","year":"2020","journal-title":"Radiology: Cardiothoracic Imaging"},{"key":"pone.0262052.ref026","unstructured":"Cohen JP, Morrison P, Dao L. COVID-19 image data collection. arXiv 200311597. 2020;."},{"key":"pone.0262052.ref027","unstructured":"Li X, Zhu D. Covid-xpert: An ai powered population screening of covid-19 cases using chest radiography images. arXiv preprint arXiv:200403042. 2020;."},{"key":"pone.0262052.ref028","doi-asserted-by":"crossref","first-page":"101794","DOI":"10.1016\/j.media.2020.101794","article-title":"Deep-covid: Predicting covid-19 from chest x-ray images using deep transfer learning","volume":"65","author":"S Minaee","year":"2020","journal-title":"Medical image analysis"},{"key":"pone.0262052.ref029","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1016\/j.patrec.2020.09.010","article-title":"Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images","volume":"138","author":"P Afshar","year":"2020","journal-title":"Pattern Recognition Letters"},{"key":"pone.0262052.ref030","first-page":"1","article-title":"Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images","author":"E Luz","year":"2021","journal-title":"Research on Biomedical Engineering"},{"key":"pone.0262052.ref031","doi-asserted-by":"crossref","unstructured":"Khobahi S, Agarwal C, Soltanalian M. CoroNet: A Deep Network Architecture for Semi-Supervised Task-Based Identification of COVID-19 from Chest X-ray Images. medRxiv. 2020;.","DOI":"10.1101\/2020.04.14.20065722"},{"key":"pone.0262052.ref032","article-title":"COVIDiagnosis-Net: Deep Bayes-SqueezeNet based Diagnostic of the Coronavirus Disease 2019 (COVID-19) from X-Ray Images","author":"F Ucar","year":"2020","journal-title":"Medical Hypotheses"},{"key":"pone.0262052.ref033","unstructured":"Yeh CF, Cheng HT, Wei A, Liu KC, Ko MC, Kuo PC, et al. A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening. arXiv preprint arXiv:200412786. 2020;."},{"key":"pone.0262052.ref034","doi-asserted-by":"crossref","first-page":"110495","DOI":"10.1016\/j.chaos.2020.110495","article-title":"CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images","volume":"142","author":"E Hussain","year":"2021","journal-title":"Chaos, Solitons & Fractals"},{"key":"pone.0262052.ref035","doi-asserted-by":"crossref","first-page":"114054","DOI":"10.1016\/j.eswa.2020.114054","article-title":"Deep learning approaches for COVID-19 detection based on chest X-ray images","volume":"164","author":"AM Ismael","year":"2021","journal-title":"Expert Systems with Applications"},{"key":"pone.0262052.ref036","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"pone.0262052.ref037","doi-asserted-by":"crossref","unstructured":"Wong A, Shafiee MJ, Chwyl B, Li F. Ferminets: Learning generative machines to generate efficient neural networks via generative synthesis. arXiv preprint arXiv:180905989. 2018;.","DOI":"10.1049\/el.2019.1719"},{"key":"pone.0262052.ref038","doi-asserted-by":"crossref","unstructured":"Cohen JP, Morrison P, Dao L, Roth K, Duong TQ, Ghassemi M. COVID-19 Image Data Collection: Prospective Predictions Are the Future. arXiv 200611988. 2020;.","DOI":"10.59275\/j.melba.2020-48g7"},{"issue":"5","key":"pone.0262052.ref039","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"DS Kermany","year":"2018","journal-title":"Cell"},{"key":"pone.0262052.ref040","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"G Litjens","year":"2017","journal-title":"Medical image analysis"},{"key":"pone.0262052.ref041","unstructured":"Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning. PMLR; 2015. p. 448\u2013456."},{"issue":"1","key":"pone.0262052.ref042","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"N Srivastava","year":"2014","journal-title":"The journal of machine learning research"},{"key":"pone.0262052.ref043","first-page":"2814","article-title":"Understanding dropout","volume-title":"Advances in neural information processing systems","author":"P Baldi","year":"2013"},{"key":"pone.0262052.ref044","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.procs.2016.07.014","article-title":"Convolutional neural networks for diabetic retinopathy","volume":"90","author":"H Pratt","year":"2016","journal-title":"Procedia Computer Science"},{"key":"pone.0262052.ref045","unstructured":"Chollet F, et al. keras, GitHub. GitHub repository, https:\/\/github.com\/fchollet\/keras. 2015;."},{"key":"pone.0262052.ref046","unstructured":"Pal S. Transfer learning and fine tuning for cross domain image classification with Keras. GitHub: transfer learning and fine tuning for cross domain image classification with Keras. 2016;."},{"key":"pone.0262052.ref047","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556. 2014;."},{"key":"pone.0262052.ref048","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. Ieee; 2009. p. 248\u2013255.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"pone.0262052.ref049","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2015. p. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"pone.0262052.ref050","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 2818\u20132826.","DOI":"10.1109\/CVPR.2016.308"},{"key":"pone.0262052.ref051","unstructured":"Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X. Improved techniques for training gans. In: Advances in neural information processing systems; 2016. p. 2234\u20132242."},{"key":"pone.0262052.ref052","doi-asserted-by":"crossref","unstructured":"Oquab M, Bottou L, Laptev I, Sivic J. Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2014. p. 1717\u20131724.","DOI":"10.1109\/CVPR.2014.222"},{"issue":"2","key":"pone.0262052.ref053","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1049\/el:20083469","article-title":"Morphological enhancement of vascular angiogram with multiscale detected by Gabor filters","volume":"44","author":"K Sun","year":"2008","journal-title":"Electronics Letters"},{"issue":"2","key":"pone.0262052.ref054","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.ejrnm.2015.01.004","article-title":"Using morphological transforms to enhance the contrast of medical images","volume":"46","author":"H Hassanpour","year":"2015","journal-title":"The Egyptian Journal of Radiology and Nuclear Medicine"},{"issue":"4","key":"pone.0262052.ref055","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"M Sokolova","year":"2009","journal-title":"Information processing & management"},{"key":"pone.0262052.ref056","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision; 2017. p. 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"},{"key":"pone.0262052.ref057","unstructured":"Hemdan EED, Shouman MA, Karar ME. Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:200311055. 2020;."},{"key":"pone.0262052.ref058","first-page":"1","article-title":"Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks","author":"ID Apostolopoulos","year":"2020","journal-title":"Physical and Engineering Sciences in Medicine"},{"key":"pone.0262052.ref059","doi-asserted-by":"crossref","unstructured":"Sethy PK, Behera SK. Detection of coronavirus disease (covid-19) based on deep features. Preprints. 2020;2020030300:2020.","DOI":"10.20944\/preprints202003.0300.v1"},{"key":"pone.0262052.ref060","doi-asserted-by":"crossref","unstructured":"Song Y, Zheng S, Li L, Zhang X, Zhang X, Huang Z, et al. Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE\/ACM Transactions on Computational Biology and Bioinformatics. 2021;.","DOI":"10.1109\/TCBB.2021.3065361"},{"key":"pone.0262052.ref061","first-page":"1","article-title":"A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)","author":"S Wang","year":"2021","journal-title":"European radiology"},{"key":"pone.0262052.ref062","doi-asserted-by":"crossref","unstructured":"Zheng C, Deng X, Fu Q, Zhou Q, Feng J, Ma H, et al. Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv. 2020;.","DOI":"10.1101\/2020.03.12.20027185"},{"issue":"11","key":"pone.0262052.ref063","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1136\/bjo.80.11.940","article-title":"Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool","volume":"80","author":"G Gardner","year":"1996","journal-title":"British journal of Ophthalmology"},{"key":"pone.0262052.ref064","doi-asserted-by":"crossref","unstructured":"Deng J, Berg AC, Li K, Fei-Fei L. What does classifying more than 10,000 image categories tell us? In: European conference on computer vision. Springer; 2010. p. 71\u201384.","DOI":"10.1007\/978-3-642-15555-0_6"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0262052","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T22:11:56Z","timestamp":1700086316000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0262052"}},"subtitle":[],"editor":[{"given":"Xiaodi","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2022,1,21]]},"references-count":64,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1,21]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0262052","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.02.06.21251271","asserted-by":"object"}]},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,21]]}}}