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The feature vectors were extracted from the images using a deep convolutional neural network, and the binary differential metaheuristic algorithm was used to select the most valuable features. The SVM classifier was then given these optimized features. For the study, a database containing images from three categories, including COVID\u201019, pneumonia, and a healthy category, included 1092 X\u2010ray samples, was used. The proposed method achieved a 99.43% accuracy, a 99.16% sensitivity, and a 99.57% specificity. Our findings indicate that the proposed method outperformed recent studies on COVID\u201019 detection using X\u2010ray images.<\/jats:p>","DOI":"10.1155\/2021\/9973277","type":"journal-article","created":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T18:32:22Z","timestamp":1633458742000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["COVID\u201019 Detection Using Deep Convolutional Neural Networks and Binary Differential Algorithm\u2010Based Feature Selection from X\u2010Ray Images"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3825-7285","authenticated-orcid":false,"given":"Mohammad Saber","family":"Iraji","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8548-976X","authenticated-orcid":false,"given":"Mohammad-Reza","family":"Feizi-Derakhshi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0779-6027","authenticated-orcid":false,"given":"Jafar","family":"Tanha","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,10,5]]},"reference":[{"key":"e_1_2_13_1_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-71294-2"},{"key":"e_1_2_13_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.04.019"},{"key":"e_1_2_13_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2011.11.036"},{"key":"e_1_2_13_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2459064"},{"key":"e_1_2_13_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1428-9"},{"key":"e_1_2_13_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICBME49163.2019.9030395"},{"key":"e_1_2_13_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2011.01.015"},{"key":"e_1_2_13_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-39442-4_44"},{"key":"e_1_2_13_9_2","unstructured":"HemdanE. 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