{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T11:47:40Z","timestamp":1784548060803,"version":"3.55.0"},"reference-count":47,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,6,5]],"date-time":"2021-06-05T00:00:00Z","timestamp":1622851200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People's Republic of China","doi-asserted-by":"publisher","award":["1-441-35"],"award-info":[{"award-number":["1-441-35"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Healthcare Engineering"],"published-print":{"date-parts":[[2021,6,5]]},"abstract":"<jats:p>The world is experiencing an unprecedented crisis due to the coronavirus disease (COVID-19) outbreak that has affected nearly 216 countries and territories across the globe. Since the pandemic outbreak, there is a growing interest in computational model-based diagnostic technologies to support the screening and diagnosis of COVID-19 cases using medical imaging such as chest X-ray (CXR) scans. It is discovered in initial studies that patients infected with COVID-19 show abnormalities in their CXR images that represent specific radiological patterns. Still, detection of these patterns is challenging and time-consuming even for skilled radiologists. In this study, we propose a novel convolutional neural network- (CNN-) based deep learning fusion framework using the transfer learning concept where parameters (weights) from different models are combined into a single model to extract features from images which are then fed to a custom classifier for prediction. We use gradient-weighted class activation mapping to visualize the infected areas of CXR images. Furthermore, we provide feature representation through visualization to gain a deeper understanding of the class separability of the studied models with respect to COVID-19 detection. Cross-validation studies are used to assess the performance of the proposed models using open-access datasets containing healthy and both COVID-19 and other pneumonia infected CXR images. Evaluation results show that the best performing fusion model can attain a classification accuracy of 95.49% with a high level of sensitivity and specificity.<\/jats:p>","DOI":"10.1155\/2021\/5513679","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T16:39:50Z","timestamp":1623083990000},"page":"1-16","source":"Crossref","is-referenced-by-count":39,"title":["Artificial Neural Network-Based Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8050-8431","authenticated-orcid":true,"given":"Mohammad","family":"Shorfuzzaman","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21974, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6019-7245","authenticated-orcid":true,"given":"Mehedi","family":"Masud","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21974, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1609-9995","authenticated-orcid":true,"given":"Hesham","family":"Alhumyani","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif 21974, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4915-8426","authenticated-orcid":true,"given":"Divya","family":"Anand","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab 144411, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6571-327X","authenticated-orcid":true,"given":"Aman","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab 144411, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1038\/s41564-020-0695-z"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jare.2020.03.005"},{"key":"3","volume-title":"Director-General\u2019s Opening Remarks at the Media Briefing on COVID","author":"World Health Organization","year":"2020"},{"key":"4","volume-title":"COVID-19 Disease 2019 (COVID-19): Weekly Epidemiological and Operational Updates","author":"World Health Organization","year":"2020"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200527"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1145\/3241056"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.06.027"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.suscom.2019.07.003"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.3390\/app10020559"},{"issue":"1","key":"10","article-title":"Intelligent techniques and applications in liver disorders: a survey","volume":"16","author":"B. 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