{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T11:28:03Z","timestamp":1772450883534,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683164","type":"print"},{"value":"9781643683171","type":"electronic"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,14]]},"abstract":"<jats:p>This paper proposes a new deep learning model to detect COVID-19 lesions in chest CT images. This method is based on the Attention U-net which uses the layer of Atrous Spatial Pyramid Pooling (ASPP) to capture the feature on various scales. It also contains an attention gate. The attention gate provides the ability to suppress irrelevant regions and focus on the useful feature in an input image. The experimental results show that this method can achieve 99.61% accuracy and 80.43% precision. They are more effectively than the baseline method on Chest CT images.<\/jats:p>","DOI":"10.3233\/faia220288","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T09:08:28Z","timestamp":1663319308000},"source":"Crossref","is-referenced-by-count":11,"title":["Segmentation on Chest CT Imaging in COVID-19 Based on the Improvement Attention U-Net Model"],"prefix":"10.3233","author":[{"given":"Nguyen N.D.","family":"Tran","sequence":"first","affiliation":[{"name":"Faculty of Mathematics and Computer Science, University of Sciences, Ho Chi Minh city, Vietnam"},{"name":"Vietnam National University, Ho Chi Minh city, Vietnam"}]},{"given":"Hien D.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, University of Information Technology, Ho Chi Minh city, Vietnam"},{"name":"Vietnam National University, Ho Chi Minh city, Vietnam"}]},{"given":"Nhan T.","family":"Huynh","sequence":"additional","affiliation":[{"name":"Campus in Ho Chi Minh City, University of Transport and Communications, Vietnam"}]},{"given":"Nha P.","family":"Tran","sequence":"additional","affiliation":[{"name":"Campus in Ho Chi Minh City, University of Transport and Communications, Vietnam"}]},{"given":"Linh V.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Idaho, Moscow, United States of America"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","New Trends in Intelligent Software Methodologies, Tools and Techniques"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220288","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T09:08:29Z","timestamp":1663319309000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220288"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,14]]},"ISBN":["9781643683164","9781643683171"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220288","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,14]]}}}