{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:17:34Z","timestamp":1787008654974,"version":"build-2736575974"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T00:00:00Z","timestamp":1646352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The coronavirus disease 2019 (COVID-19) pandemic has been a globally dangerous crisis that causes an increasingly high death rate. Applying machine learning to the computed-tomography (CT)-based COVID-19 diagnosis is essential and attracts the attention of the research community. This paper introduces an approach for simultaneously identifying COVID-19 disease and segmenting its manifestations on lung images. The proposed method is an asymmetric U-Net-like model improved with skip connections. The experiment was conducted on a light-weighted feature extractor called CRNet with a feature enhancement technique called atrous spatial pyramid pooling. Classifying between COVID-19 and non-COVID-19 cases recorded the highest mean scores of 97.1, 94.4, and 97.0% for accuracy, dice similarity coefficient (DSC) and F1 score, respectively. Alternatively, the respective highest mean scores of the classification between COVID-19 and community-acquired pneumonia were 99.89, 99.79, and 99.97%. The lesion segmentation performance was with the highest mean of 99.6 and 84.7% for, respectively, accuracy and DSC.<\/jats:p>","DOI":"10.1093\/comjnl\/bxac015","type":"journal-article","created":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T15:06:52Z","timestamp":1644160012000},"page":"1366-1375","source":"Crossref","is-referenced-by-count":7,"title":["A Novel Approach For CT-Based COVID-19 Classification and Lesion Segmentation Based On Deep Learning"],"prefix":"10.1093","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3729-5343","authenticated-orcid":false,"given":"Hieu Minh","family":"Truong","sequence":"first","affiliation":[{"name":"Faculty of Engineering , Vietnamese-German University, Binh Duong, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2097-0704","authenticated-orcid":false,"given":"Hieu Trung","family":"Huynh","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology , Industrial University of Ho Chi Minh city, Ho Chi Minh city, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"2023062010044767400_ref1","doi-asserted-by":"crossref","first-page":"109009","DOI":"10.1016\/j.ejrad.2020.109009","article-title":"Chest CT findings of COVID-19 pneumonia by duration of symptoms","volume":"127","author":"Ding","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"2023062010044767400_ref2","doi-asserted-by":"crossref","first-page":"E32","DOI":"10.1148\/radiol.2020200642","article-title":"Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases","volume":"296","author":"Ai","year":"2020","journal-title":"Radiology"},{"key":"2023062010044767400_ref3","first-page":"1","volume-title":"Proceedings of RIVF2019","author":"Huynh","year":"2019"},{"key":"2023062010044767400_ref4","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3322\/caac.21552","article-title":"Artificial intelligence in cancer imaging: Clinical challenges and applications","volume":"69","author":"Bi","year":"2019","journal-title":"CA Cancer J. Clin."},{"key":"2023062010044767400_ref5","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/B978-0-12-824477-7.00005-5","volume-title":"Foundations of Artificial Intelligence in Healthcare and Bioscience","author":"Catania","year":"2021"},{"key":"2023062010044767400_ref6","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.ejmp.2021.02.024","article-title":"Requirements and reliability of AI in the medical context","volume":"83","author":"Balagurunathan","year":"2021","journal-title":"Phys. Med."},{"key":"2023062010044767400_ref7","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s12539-020-00410-7","article-title":"A Radiomics signature to quantitatively analyze COVID-19-infected pulmonary lesions","volume":"13","author":"Qiu","year":"2021","journal-title":"Interdiscip. 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Lecture Notes in Computer Science","author":"Ronneberger","year":"2015"},{"key":"2023062010044767400_ref11","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"2023062010044767400_ref12","article-title":"Deep residual learning for image recognition","author":"He","year":"2015"},{"key":"2023062010044767400_ref13","article-title":"Densely connected convolutional networks","author":"Huang","year":"2016"},{"key":"2023062010044767400_ref14","volume-title":"Int. Conf. on Machine Learning 2019","author":"Tan","year":"2019"},{"key":"2023062010044767400_ref15","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2023062010044767400_ref16","article-title":"Rethinking Atrous convolution for semantic image segmentation","author":"Chen","year":"2017"},{"key":"2023062010044767400_ref17","article-title":"The European Institute for Biomedical Imaging Research","author":"COVID-19 imaging datasets","year":"2021"},{"key":"2023062010044767400_ref18","author":"Italian Society of Radiology","year":"2020"},{"key":"2023062010044767400_ref19","author":"Radiopedia","year":"2020"},{"key":"2023062010044767400_ref20","volume-title":"Nanjing University of Science and Technology","author":"Ma","year":"2020"},{"key":"2023062010044767400_ref21","author":"Moscow Health Care","year":"2020"},{"key":"2023062010044767400_ref22","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1038\/s41597-021-00900-3","article-title":"COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning","volume":"8","author":"Afshar","year":"2021","journal-title":"Scientific Data"},{"key":"2023062010044767400_ref23","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1186\/s41747-020-00173-2","article-title":"Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem","volume":"4","author":"Hofmanninger","year":"2020","journal-title":"European Radiology Experimental"},{"key":"2023062010044767400_ref24","author":"Albumentations","year":"2021"},{"key":"2023062010044767400_ref25","author":"The Jupyter Notebook","year":"2021"},{"key":"2023062010044767400_ref26","author":"PyTorch","year":"2021"},{"key":"2023062010044767400_ref27","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV.2015.123","article-title":"Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification","author":"He","year":"2015"},{"key":"2023062010044767400_ref28","article-title":"A novel focal Tversky loss function with improved attention U-net for lesion segmentation","author":"Abraham","year":"2018"},{"key":"2023062010044767400_ref29","article-title":"SGDR: Stochastic gradient descent with warm restarts","author":"Loshchilov","year":"2016"}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/66\/6\/1366\/50643585\/bxac015.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/66\/6\/1366\/50643585\/bxac015.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T21:22:45Z","timestamp":1700169765000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/article\/66\/6\/1366\/6542227"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,4]]},"references-count":29,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3,4]]},"published-print":{"date-parts":[[2023,6,19]]}},"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxac015","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"value":"0010-4620","type":"print"},{"value":"1460-2067","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,6]]},"published":{"date-parts":[[2022,3,4]]}}}