{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:18:54Z","timestamp":1778257134672,"version":"3.51.4"},"reference-count":38,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,5,15]],"date-time":"2021-05-15T00:00:00Z","timestamp":1621036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["International Journal of Biomedical Imaging"],"published-print":{"date-parts":[[2021,5,15]]},"abstract":"<jats:p>The novel coronavirus disease 2019 (COVID-19) is a contagious disease that has caused thousands of deaths and infected millions worldwide. Thus, various technologies that allow for the fast detection of COVID-19 infections with high accuracy can offer healthcare professionals much-needed help. This study is aimed at evaluating the effectiveness of the state-of-the-art pretrained Convolutional Neural Networks (CNNs) on the automatic diagnosis of COVID-19 from chest X-rays (CXRs). The dataset used in the experiments consists of 1200 CXR images from individuals with COVID-19, 1345 CXR images from individuals with viral pneumonia, and 1341 CXR images from healthy individuals. In this paper, the effectiveness of artificial intelligence (AI) in the rapid and precise identification of COVID-19 from CXR images has been explored based on different pretrained deep learning algorithms and fine-tuned to maximise detection accuracy to identify the best algorithms. The results showed that deep learning with X-ray imaging is useful in collecting critical biological markers associated with COVID-19 infections. VGG16 and MobileNet obtained the highest accuracy of 98.28%. However, VGG16 outperformed all other models in COVID-19 detection with an accuracy, F1 score, precision, specificity, and sensitivity of 98.72%, 97.59%, 96.43%, 98.70%, and 98.78%, respectively. The outstanding performance of these pretrained models can significantly improve the speed and accuracy of COVID-19 diagnosis. However, a larger dataset of COVID-19 X-ray images is required for a more accurate and reliable identification of COVID-19 infections when using deep transfer learning. This would be extremely beneficial in this pandemic when the disease burden and the need for preventive measures are in conflict with the currently available resources.<\/jats:p>","DOI":"10.1155\/2021\/8828404","type":"journal-article","created":{"date-parts":[[2021,5,15]],"date-time":"2021-05-15T15:35:08Z","timestamp":1621092908000},"page":"1-9","source":"Crossref","is-referenced-by-count":75,"title":["Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8722-4661","authenticated-orcid":true,"given":"Mundher Mohammed","family":"Taresh","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Hunan University, Changsha 400013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7913-2740","authenticated-orcid":true,"given":"Ningbo","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan University, Changsha 400013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3395-3815","authenticated-orcid":true,"given":"Talal Ahmed Ali","family":"Ali","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Hunan University, Changsha 400013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5979-6232","authenticated-orcid":true,"given":"Asaad Shakir","family":"Hameed","sequence":"additional","affiliation":[{"name":"Department of Mathematics, General Directorate of Thi-Qar Education, Ministry of Education, Thi-Qar, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3038-8507","authenticated-orcid":true,"given":"Modhi Lafta","family":"Mutar","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020201754"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200230"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(20)30086-4"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2462070712"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1002\/jmv.25722"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1007\/s11547-020-01200-3"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1186\/s43055-020-00296-x"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.12669\/pjms.36.covid19-s4.2778"},{"key":"9","article-title":"Automatic detection of coronavirus disease (covid-19) using X-ray images and deep convolutional neural networks","author":"A. Narin","year":"2020"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1007\/s13246-020-00865-4"},{"key":"11","article-title":"Covidx-net: a framework of deep learning classifiers to diagnose covid-19 in x-ray images","author":"E. E.-D. Hemdan","year":"2020"},{"key":"12","volume-title":"Detection of coronavirus disease (Covid-19) based on deep features","author":"P. K. Sethy","year":"2020"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103792"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.2174\/1573405616666200604163954"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-05275-y"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.3390\/app10134640"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01900-3"},{"key":"18","article-title":"Covid-resnet: a deep learning framework for screening of covid19 from radiographs","author":"M. Farooq","year":"2020"},{"key":"19","article-title":"Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms","author":"H. S. Maghdid","year":"2020"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3010287"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.3010287"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105581"},{"key":"23","doi-asserted-by":"crossref","article-title":"Grad-cam: visual explanations from deep networks via gradient-based localization","author":"R. R. Selvaraju","DOI":"10.1109\/ICCV.2017.74"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.02.010"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.3390\/app8101715"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.3390\/app10020559"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3000949"},{"key":"28","article-title":"Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning","author":"P. Rajpurkar","year":"2017"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.05.076"},{"key":"30","first-page":"1","article-title":"Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images","author":"K. C. Kamal","year":"2021","journal-title":"Signal, Image and Video Processing"},{"key":"31","article-title":"Improving neural networks by preventing co-adaptation of feature detectors","author":"G. E. Hinton","year":"2012"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1021\/ci0342472"},{"key":"33","article-title":"Rmsprop: divide the gradient by a running average of its recent magnitude","volume-title":"Coursera: neural networks for machine learning","author":"T. Tieleman","year":"2012"},{"key":"34","article-title":"Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation","author":"D. M. Powers","year":"2020"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0235187"},{"key":"36","volume-title":"COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images","author":"A. Z. Khuzani","year":"2020"},{"issue":"1","key":"37","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":"38","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110122"}],"container-title":["International Journal of Biomedical Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijbi\/2021\/8828404.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijbi\/2021\/8828404.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijbi\/2021\/8828404.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T09:57:22Z","timestamp":1623664642000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/ijbi\/2021\/8828404\/"}},"subtitle":[],"editor":[{"given":"Jyh-Cheng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,5,15]]},"references-count":38,"alternative-id":["8828404","8828404"],"URL":"https:\/\/doi.org\/10.1155\/2021\/8828404","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.08.25.20182170","asserted-by":"object"}]},"ISSN":["1687-4196","1687-4188"],"issn-type":[{"value":"1687-4196","type":"electronic"},{"value":"1687-4188","type":"print"}],"subject":[],"published":{"date-parts":[[2021,5,15]]}}}