{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T15:20:18Z","timestamp":1781623218577,"version":"3.54.5"},"reference-count":62,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,11]],"date-time":"2021-01-11T00:00:00Z","timestamp":1610323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4%, 99.6%, 99.8%, 99.6%, and 99.4% on the SARS-CoV-2 dataset, and 92.9%, 91.3%, 93.7%, 92.2%, and 92.5% on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models\u2019 predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists.<\/jats:p>","DOI":"10.3390\/s21020455","type":"journal-article","created":{"date-parts":[[2021,1,11]],"date-time":"2021-01-11T11:36:11Z","timestamp":1610364971000},"page":"455","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":175,"title":["Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9942-8642","authenticated-orcid":false,"given":"Hammam","family":"Alshazly","sequence":"first","affiliation":[{"name":"Institute for Neuro- and Bioinformatics, University of L\u00fcbeck, 23562 L\u00fcbeck, Germany"},{"name":"Mathematics Department, Faculty of Science, South Valley University, Qena 83523, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7039-5189","authenticated-orcid":false,"given":"Christoph","family":"Linse","sequence":"additional","affiliation":[{"name":"Institute for Neuro- and Bioinformatics, University of L\u00fcbeck, 23562 L\u00fcbeck, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8556-2472","authenticated-orcid":false,"given":"Erhardt","family":"Barth","sequence":"additional","affiliation":[{"name":"Institute for Neuro- and Bioinformatics, University of L\u00fcbeck, 23562 L\u00fcbeck, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4539-4475","authenticated-orcid":false,"given":"Thomas","family":"Martinetz","sequence":"additional","affiliation":[{"name":"Institute for Neuro- and Bioinformatics, University of L\u00fcbeck, 23562 L\u00fcbeck, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.3201\/eid2606.200239","article-title":"Community transmission of severe acute respiratory syndrome coronavirus 2, Shenzhen, China, 2020","volume":"26","author":"Liu","year":"2020","journal-title":"Emerg. 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