{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T12:47:26Z","timestamp":1771850846900,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T00:00:00Z","timestamp":1630368000000},"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>Since the appearance of the COVID-19 pandemic (at the end of 2019, Wuhan, China), the recognition of COVID-19 with medical imaging has become an active research topic for the machine learning and computer vision community. This paper is based on the results obtained from the 2021 COVID-19 SPGC challenge, which aims to classify volumetric CT scans into normal, COVID-19, or community-acquired pneumonia (Cap) classes. To this end, we proposed a deep-learning-based approach (CNR-IEMN) that consists of two main stages. In the first stage, we trained four deep learning architectures with a multi-tasks strategy for slice-level classification. In the second stage, we used the previously trained models with an XG-boost classifier to classify the whole CT scan into normal, COVID-19, or Cap classes. Our approach achieved a good result on the validation set, with an overall accuracy of 87.75% and 96.36%, 52.63%, and 95.83% sensitivities for COVID-19, Cap, and normal, respectively. On the other hand, our approach achieved fifth place on the three test datasets of SPGC in the COVID-19 challenge, where our approach achieved the best result for COVID-19 sensitivity. In addition, our approach achieved second place on two of the three testing sets.<\/jats:p>","DOI":"10.3390\/s21175878","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T22:58:15Z","timestamp":1630450695000},"page":"5878","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5077-4862","authenticated-orcid":false,"given":"Fares","family":"Bougourzi","sequence":"first","affiliation":[{"name":"Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Riccardo","family":"Contino","sequence":"additional","affiliation":[{"name":"Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1073-2390","authenticated-orcid":false,"given":"Cosimo","family":"Distante","sequence":"additional","affiliation":[{"name":"Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy"},{"name":"Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdelmalik","family":"Taleb-Ahmed","sequence":"additional","affiliation":[{"name":"Univ. Polytechnique Hauts-de-France, Univ. Lille, CNRS, Centrale Lille, UMR 8520, F-59313 Valenciennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1007\/s10238-020-00648-x","article-title":"Organ-specific manifestations of COVID-19 infection","volume":"20","author":"Gavriatopoulou","year":"2020","journal-title":"Clin. Exp. Med."},{"key":"ref_2","unstructured":"(2021, June 15). World Health Organization. Available online: https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Vantaggiato, E., Paladini, E., Bougourzi, F., Distante, C., Hadid, A., and Taleb-Ahmed, A. (2021). COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases. 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