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The lung region abnormality detection models have pretty good average precision. For example, the YOLOv5 model outperforms both in terms of training time, prediction, and accuracy, with the AP@.5 and AP@.5:.95 metric values, 0.616 and 0.322 on 2,500 images of 5 abnormalities (aortic enlargement, cardiomegaly, lung opacity, pleural effusion, and pulmonary fibrosis). <\/jats:p>","DOI":"10.1142\/s2196888823500094","type":"journal-article","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T07:14:10Z","timestamp":1688022850000},"page":"499-515","source":"Crossref","is-referenced-by-count":14,"title":["Anomalies Detection in Chest X-Rays Images Using Faster R-CNN and YOLO"],"prefix":"10.1142","volume":"10","author":[{"given":"Hai Thanh","family":"Nguyen","sequence":"first","affiliation":[{"name":"College of Information and Communication Technology, Can Tho University, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"My N.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"College of Information and Communication Technology, Can Tho University, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linh Duong","family":"Phung","sequence":"additional","affiliation":[{"name":"College of Information and Communication Technology, Can Tho University, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linh Thuy Thi","family":"Pham","sequence":"additional","affiliation":[{"name":"Can Tho University of Technology, Can Tho, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2023,8,5]]},"reference":[{"key":"S2196888823500094BIB001","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.8.5.052109"},{"issue":"1","key":"S2196888823500094BIB002","first-page":"65","volume":"3","author":"Katti G.","year":"2011","journal-title":"Int. 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