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However, computed tomography (CT) image segmentation can make clinicians quickly identify the COVID\u201019\u2010infected regions. Accurate segmentation infection area of COVID\u201019 can contribute screen confirmed cases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We designed a segmentation network for COVID\u201019\u2010infected regions in CT images. To begin with, multilayered features were extracted by the backbone network of Res2Net. Subsequently, edge features of the infected regions in the low\u2010level feature <jats:italic>f<\/jats:italic><jats:sub>2<\/jats:sub> were extracted by the edge attention module. Second, we carefully designed the structure of the attention position module (APM) to extract high\u2010level feature <jats:italic>f<\/jats:italic><jats:sub>5<\/jats:sub> and detect infected regions. Finally, we proposed a context exploration module consisting of two parallel explore blocks, which can remove some false positives and false negatives to reach more accurate segmentation results.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Experimental results show that, on the public COVID\u201019 dataset, the Dice, sensitivity, specificity, , , and mean absolute error (<jats:italic>MAE<\/jats:italic>) of our method are 0.755, 0.751, 0.959, 0.795, 0.919, and 0.060, respectively. Compared with the latest COVID\u201019 segmentation model Inf\u2010Net, the Dice similarity coefficient of our model has increased by 7.3%; the sensitivity (Sen) has increased by 5.9%. On contrary, the <jats:italic>MAE<\/jats:italic> has dropped by 2.2%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our method performs well on COVID\u201019 CT image segmentation. We also find that our method is so portable that can be suitable for various current popular networks. In a word, our method can help screen people infected with COVID\u201019 effectively and save the labor power of clinicians and radiologists.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15882","type":"journal-article","created":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T08:59:30Z","timestamp":1659430770000},"page":"7583-7595","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["COVID\u201019 CT image segmentation based on improved Res2Net"],"prefix":"10.1002","volume":"49","author":[{"given":"Shangwang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering Henan Normal University  Xinxiang Henan China"},{"name":"Engineering Lab of Intelligence Business &amp; Internet of Things  Xinxiang Henan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiufang","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering Henan Normal University  Xinxiang Henan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tongbo","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering Henan Normal University  Xinxiang Henan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering Henan Normal University  Xinxiang Henan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changgeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering Henan Normal University  Xinxiang Henan China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,8,10]]},"reference":[{"issue":"18","key":"e_1_2_7_2_1","first-page":"1843","article-title":"Detection of 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