{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T20:40:40Z","timestamp":1779914440526,"version":"3.53.1"},"reference-count":51,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T00:00:00Z","timestamp":1634860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Currently, the world is still facing a COVID-19 (coronavirus disease 2019) classified as a highly infectious disease due to its rapid spreading. The shortage of X-ray machines may lead to critical situations and delay the diagnosis results, increasing the number of deaths. Therefore, the exploitation of deep learning (DL) and optimization algorithms can be advantageous in early diagnosis and COVID-19 detection. In this paper, we propose a framework for COVID-19 images classification using hybridization of DL and swarm-based algorithms. The MobileNetV3 is used as a backbone feature extraction to learn and extract relevant image representations as a DL model. As a swarm-based algorithm, the Aquila Optimizer (Aqu) is used as a feature selector to reduce the dimensionality of the image representations and improve the classification accuracy using only the most essential selected features. To validate the proposed framework, two datasets with X-ray and CT COVID-19 images are used. The obtained results from the experiments show a good performance of the proposed framework in terms of classification accuracy and dimensionality reduction during the feature extraction and selection phases. The Aqu feature selection algorithm achieves accuracy better than other methods in terms of performance metrics.<\/jats:p>","DOI":"10.3390\/e23111383","type":"journal-article","created":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T09:18:09Z","timestamp":1634894289000},"page":"1383","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":106,"title":["Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7682-6269","authenticated-orcid":false,"given":"Mohamed","family":"Abd Elaziz","sequence":"first","affiliation":[{"name":"Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt"},{"name":"Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdelghani","family":"Dahou","sequence":"additional","affiliation":[{"name":"Mathematics and Computer Science Department, University of Ahmed DRAIA, Adrar 01000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5779-0451","authenticated-orcid":false,"given":"Naser A.","family":"Alsaleh","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0944-4938","authenticated-orcid":false,"given":"Ammar H.","family":"Elsheikh","sequence":"additional","affiliation":[{"name":"Department of Production Engineering and Mechanical Design, Faculty of Engineering, Tanta University, Tanta 31527, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amal I.","family":"Saba","sequence":"additional","affiliation":[{"name":"Department of Histology, Faculty of Medicine, Tanta University, Tanta 31527, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4896-6835","authenticated-orcid":false,"given":"Mahmoud","family":"Ahmadein","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia"},{"name":"Department of Production Engineering and Mechanical Design, Faculty of Engineering, Tanta University, Tanta 31527, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.ijid.2020.03.003","article-title":"Recurrence of positive SARS-CoV-2 RNA in COVID-19: A case report","volume":"93","author":"Chen","year":"2020","journal-title":"Int. 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