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In response to blurred boundaries and high variability characteristic of lesion areas in COVID\u201019 CT images, we introduce CDSE\u2010UNet: a novel UNet\u2010based segmentation model that integrates Canny operator edge detection and a Dual\u2010Path SENet Feature Fusion Block (DSBlock). This model enhances the standard UNet architecture by employing the Canny operator for edge detection in sample images, paralleling this with a similar network structure for semantic feature extraction. A key innovation is the DSBlock, applied across corresponding network layers to effectively combine features from both image paths. Moreover, we have developed a Multiscale Convolution Block (MSCovBlock), replacing the standard convolution in UNet, to adapt to the varied lesion sizes and shapes. This addition not only aids in accurately classifying lesion edge pixels but also significantly improves channel differentiation and expands the capacity of the model. Our evaluations on public datasets demonstrate CDSE\u2010UNet\u2019s superior performance over other leading models. Specifically, CDSE\u2010UNet achieved an accuracy of 0.9929, a recall of 0.9604, a DSC of 0.9063, and an IoU of 0.8286, outperforming UNet, Attention\u2010UNet, Trans\u2010Unet, Swin\u2010Unet, and Dense\u2010UNet in these metrics.<\/jats:p>","DOI":"10.1155\/ijbi\/9175473","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T02:03:31Z","timestamp":1742177011000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["CDSE\u2010UNet: Enhancing COVID\u201019 CT Image Segmentation With Canny Edge Detection and Dual\u2010Path SENet Feature Fusion"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2362-3609","authenticated-orcid":false,"given":"Jiao","family":"Ding","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2146-4918","authenticated-orcid":false,"given":"Jie","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5484-3183","authenticated-orcid":false,"given":"Renrui","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9757-4198","authenticated-orcid":false,"given":"Li","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,3,16]]},"reference":[{"key":"e_1_2_14_1_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.3786"},{"key":"e_1_2_14_2_2","article-title":"Liver CT image segmentation method based on MSFA-net","volume":"17","author":"Shen H.","year":"2023","journal-title":"Journal of Frontiers of Computer Science & Technology"},{"key":"e_1_2_14_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijid.2020.06.027"},{"key":"e_1_2_14_4_2","doi-asserted-by":"publisher","DOI":"10.15983\/j.cnki.jsnu.2022108"},{"key":"e_1_2_14_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-023-05633-1"},{"key":"e_1_2_14_6_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1018628609742"},{"key":"e_1_2_14_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/21.97458"},{"key":"e_1_2_14_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"e_1_2_14_9_2","doi-asserted-by":"crossref","unstructured":"LongJ. 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