{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T15:49:59Z","timestamp":1774021799728,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T00:00:00Z","timestamp":1728777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jordan University of Science and Technology","award":["20190306"],"award-info":[{"award-number":["20190306"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The global spread of Coronavirus (COVID-19) has prompted imperative research into scalable and effective detection methods to curb its outbreak. The early diagnosis of COVID-19 patients has emerged as a pivotal strategy in mitigating the spread of the disease. Automated COVID-19 detection using Chest X-ray (CXR) imaging has significant potential for facilitating large-scale screening and epidemic control efforts. This paper introduces a novel approach that employs state-of-the-art Convolutional Neural Network models (CNNs) for accurate COVID-19 detection. The employed datasets each comprised 15,000 X-ray images. We addressed both binary (Normal vs. Abnormal) and multi-class (Normal, COVID-19, Pneumonia) classification tasks. Comprehensive evaluations were performed by utilizing six distinct CNN-based models (Xception, Inception-V3, ResNet50, VGG19, DenseNet201, and InceptionResNet-V2) for both tasks. As a result, the Xception model demonstrated exceptional performance, achieving 98.13% accuracy, 98.14% precision, 97.65% recall, and a 97.89% F1-score in binary classification, while in multi-classification it yielded 87.73% accuracy, 90.20% precision, 87.73% recall, and an 87.49% F1-score. Moreover, the other utilized models, such as ResNet50, demonstrated competitive performance compared with many recent works.<\/jats:p>","DOI":"10.3390\/jimaging10100250","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T05:47:58Z","timestamp":1728884878000},"page":"250","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Enhanced COVID-19 Detection from X-ray Images with Convolutional Neural Network and Transfer Learning"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4722-6632","authenticated-orcid":false,"given":"Qanita","family":"Bani Baker","sequence":"first","affiliation":[{"name":"Faculty of Computer and Information Technology, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahmoud","family":"Hammad","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Technology, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed","family":"Al-Smadi","sequence":"additional","affiliation":[{"name":"Digital Learning and Online Education Office (DLOE), Qatar University, Doha 2713, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heba","family":"Al-Jarrah","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Technology, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rahaf","family":"Al-Hamouri","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Technology, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6793-4527","authenticated-orcid":false,"given":"Sa\u2019ad A.","family":"Al-Zboon","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Technology, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,13]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2020). Coronavirus Disease 2019. 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