{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T08:10:08Z","timestamp":1768205408447,"version":"3.49.0"},"reference-count":40,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T00:00:00Z","timestamp":1766448000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Accurate segmentation of skin lesion images is essential for diagnosing and treating skin disorders. While current research primarily aims to enhance segmentation accuracy through the use of complex network models, the large size of these models restricts their practical application in clinical settings. To address this challenge, we propose SMedt (skin\u2010medical transformer), a high\u2010precision, parameter\u2010efficient model for skin lesion image segmentation. SMedt combines CNN and transformer architectures within a dual\u2010branch structure to extract both global and local features effectively. The model's global branch employs a dual\u2010attention mechanism that integrates channel and spatial attention, along with skip connection cross attention (SCCA) between the encoder and decoder layers, to enhance global feature decoding. The local branch incorporates an All\u2010aggregation decoder (all decoder) method, enabling the capture of multi\u2010scale features, while pyramid stacking improves the extraction of local features across different channel dimensions. We evaluated SMedt on the ISIC2016 and ISIC2018 datasets. On the ISIC2016 dataset, the model achieved a 0.17% improvement in the Dice coefficient over the second\u2010best model, FAT\u2010Net, while reducing model parameters by 91%. On the ISIC2018 dataset, SMedt improved the Dice coefficient by 1.1% over the state\u2010of\u2010the\u2010art Efficient UNet, while reducing model parameters by 73.26%. Compared with the latest lightweight model UCM\u2010Net, SMedt improves segmentation accuracy (Dice) by 1.92% on ISIC2018, achieving a better balance between accuracy and model size. By leveraging the strengths of both CNN and Transformer models, SMedt maintains high segmentation accuracy while keeping a low parameter count, thereby enhancing diagnostic accuracy and clinical efficiency.<\/jats:p>","DOI":"10.1002\/cpe.70507","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T12:57:48Z","timestamp":1766494668000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lightweight Skin Lesion Images Segmentation Based on\u00a0\n                    <scp>CNN<\/scp>\n                    and Transformer"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2573-3317","authenticated-orcid":false,"given":"Tuoyu","family":"Ouyang","sequence":"first","affiliation":[{"name":"College of Electrical and Information Engineering Hunan University  Changsha China"},{"name":"State Grid Hunan Electric Power Company Limited Loudi Power Supply Company  Loudi Hunan China"}]},{"given":"Huimin","family":"Quan","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering Hunan University  Changsha China"}]},{"given":"Guocai","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering Hunan University  Changsha China"}]},{"given":"Shuyi","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Department of Biology Shenzhen MSU\u2010BIT University  Shenzhen Guangdong China"}]}],"member":"311","published-online":{"date-parts":[[2025,12,23]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21708"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1440-0960.2005.00189.x"},{"key":"e_1_2_9_4_1","first-page":"234","volume-title":"U\u2010Net: Convolutional Net\u2010Works for Biomedical Image Segmentation[C]\/\/Proc of Intern\u2010Ational Conference on Medical Image Computing and Comput\u2010Er\u2010Assisted Intervention","author":"Ronneberger O.","year":"2015"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"e_1_2_9_7_1","unstructured":"A.Vaswani N. 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