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However, small size, weak contrast, irregular morphology, indistinct boundaries, and interference from surrounding gastric tissues make automatic segmentation difficult. To address these challenges, this study proposes GSFDoubleU\u2010Net, a lightweight dual\u2010branch architecture for gastric polyp segmentation. The first branch employs a pretrained VGG19 encoder and a Lightweight Atrous Spatial Pyramid Pooling (L\u2010ASPP) module to capture multi\u2010scale contextual information, whereas the second lightweight encoder\u2013decoder branch focuses on contextual perception and fine\u2010grained feature recovery with limited computational overhead. A Gated Self\u2010Attention Feature Fusion (GSFF) module is further introduced to integrate complementary dual\u2010branch features, model long\u2010range dependencies, suppress irrelevant background responses, and refine lesion boundaries. On CVC\u2010ClinicDB, GSFDoubleU\u2010Net achieved a Dice score of 0.957 and a Precision of 0.970; on Kvasir\u2010SEG, it obtained a Dice score of 0.949, an mIoU of 0.898, and a Precision of 0.953. When evaluated on the independent internal clinical dataset, the model achieved a Dice score of 0.857 and an mIoU of 0.832, while obtaining lower HD and MSD values than the compared methods. These results indicate that GSFDoubleU\u2010Net provides a favorable balance between segmentation accuracy, boundary delineation, and computational efficiency, and may serve as a useful framework for computer\u2010assisted endoscopic image analysis.<\/jats:p>","DOI":"10.1002\/ima.70432","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T03:49:11Z","timestamp":1787543351000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>GSFDoubleU<\/scp>\n                    \u2010Net: A Dual\u2010Branch Architecture Incorporating Gated Self\u2010Attention Fusion for Enhanced Gastric Polyp Image Segmentation"],"prefix":"10.1002","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4423-1180","authenticated-orcid":false,"given":"Wenbin","family":"Yang","sequence":"first","affiliation":[{"name":"School of Science Xi'an University of Posts and Telecommunications  Xi'an Shaanxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Computer Science &amp; Technology Xi'an University of Posts and Telecommunications  Xi'an Shaanxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1127-0252","authenticated-orcid":false,"given":"Xiaojuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Honghui Hospital Xi'an Jiaotong University  Xi'an Shaanxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,23]]},"reference":[{"key":"e_1_2_10_2_1","first-page":"544","volume-title":"World Cancer Report 2014","author":"Stewart B.","year":"2015"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10120-011-0085-6"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms22126548"},{"key":"e_1_2_10_5_1","first-page":"470","article-title":"Analysis of Risk Factors for the Development of Colorectal Adenomatous Polyps","volume":"26","author":"Zhu Z.","year":"2021","journal-title":"Chinese Journal of Anatomy and Clinical Science"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/MOG.0000000000000230"},{"key":"e_1_2_10_7_1","doi-asserted-by":"crossref","unstructured":"O.Ronneberger P.Fischer andT.Brox \u201cU\u2010Net: Convolutional networks for biomedical image segmentation \u201din: Proc. 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