{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T05:11:51Z","timestamp":1741237911958,"version":"3.38.0"},"reference-count":40,"publisher":"National Library of Serbia","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:p>Since its emergence at the end of 2019, SARS-CoV-2 has infected millions worldwide, challenging healthcare systems globally. This has prompted many researchers to explore how machine learning can assist clinicians in diagnosing infections caused by SARS-CoV-2. Building on previous studies, we propose a novel deep learning framework designed for segmenting lesions evident in Computed Tomography (CT) scans. For this work, we utilized a dataset consisting of 20 CT scans annotated by experts and performed training, validation, and external evaluation of the deep learning models we implemented, using a 5-fold cross-validation scheme. When splitting data by slice, our optimal model achieved noteworthy performance, attaining a Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) score of 0.8644 and 0.7612 respectively, during the validation phase. In the external evaluation phase, the model maintained strong performance with a DSC and an IoU score of 0.7211 and 0.5641, respectively. When splitting data by patient, our optimal model achieved a DSC score of 0.7989 and an IoU score of 0.6686 during the validation phase. During the external evaluation phase, the model maintained strong performance with a DSC and IoU score of 0.7369 and 0.5837, respectively. The results of this research suggest that incorporating transfer learning along with appropriate preprocessing techniques, can contribute to achieving state-of-the-art performance in the segmentation of lesions associated with SARS-CoV-2 infections.<\/jats:p>","DOI":"10.2298\/csis240229066p","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T11:26:14Z","timestamp":1730805974000},"page":"1-32","source":"Crossref","is-referenced-by-count":0,"title":["Segmentation of COVID-19 CT lesions in CT scans through transfer learning"],"prefix":"10.2298","volume":"22","author":[{"given":"Symeon","family":"Psaraftis-Souranis","sequence":"first","affiliation":[{"name":"Department of Informatics and Computer Engineering, University of West Attica, Egaleo, Greece"}]},{"given":"Christos","family":"Troussas","sequence":"additional","affiliation":[{"name":"Department of Informatics and Computer Engineering, University of West Attica, Egaleo, Greece"}]},{"given":"Athanasios","family":"Voulodimos","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece"}]},{"given":"Cleo","family":"Sgouropoulou","sequence":"additional","affiliation":[{"name":"Department of Informatics and Computer Engineering, University of West Attica, Egaleo, Greece"}]}],"member":"1078","reference":[{"key":"ref1","unstructured":"WHO Coronavirus (COVID-19) Dashboard. 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