{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T10:10:15Z","timestamp":1753870215487,"version":"3.41.2"},"reference-count":44,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T00:00:00Z","timestamp":1738886400000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Robotics Computer Surgery"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Accurately identifying lung lesions in CT (Computed Tomography) scans remains crucial during the Coronavirus Disease 2019 (COVID\u201019) pandemic. Swarm intelligence algorithms offer promising tools for this purpose.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study compares four swarm intelligence algorithms Gravitational Search Algorithm (GSA), Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for segmenting COVID\u201019 lung lesions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>GA, GSA, and BFOA achieved accuracies exceeding 90.5%, while the PSO algorithm further improved segmentation accuracy, reaching 91.45%, with an exceptional F1 score of 95.54%. Overall, the approach achieved up to 99% segmentation accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The findings demonstrate the effectiveness of swarm and evolutionary algorithms in segmenting COVID\u201019 lesions, contributing to enhanced diagnostic accuracy and treatment efficiency.<\/jats:p><\/jats:sec>","DOI":"10.1002\/rcs.70044","type":"journal-article","created":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T12:26:26Z","timestamp":1738931186000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Refining COVID\u201019 Lesion Segmentation in Lung CT Scans Using Swarm Intelligence and Evolutionary Algorithms"],"prefix":"10.1002","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1901-8318","authenticated-orcid":false,"given":"Wafa","family":"Gtifa","sequence":"first","affiliation":[{"name":"Laboratory of Automation and Electrical Systems and Environment Monastir National School of Engineers (ENIM) University of Monastir  Monastir Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marwa","family":"Fradi","sequence":"additional","affiliation":[{"name":"Laboratory of Electronic and Microelectronic Faculty of Sciences of Monastir University of Monastir  Monastir Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anis","family":"Sakly","sequence":"additional","affiliation":[{"name":"Laboratory of Automation and Electrical Systems and Environment Monastir National School of Engineers (ENIM) University of Monastir  Monastir Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohsen","family":"Machhout","sequence":"additional","affiliation":[{"name":"Laboratory of Electronic and Microelectronic Faculty of Sciences of Monastir University of Monastir  Monastir Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,2,7]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.46234\/ccdcw2020.017"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200463"},{"volume-title":"Coronavirus Disease (Covid\u201019) Technical Guidance: The Unity Studies: Early Investigations Protocols","year":"2020","author":"W. 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