{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T11:17:45Z","timestamp":1784546265257,"version":"3.55.0"},"reference-count":60,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62461027"],"award-info":[{"award-number":["62461027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004761","name":"Natural Science Foundation of Hunan Province, China","doi-asserted-by":"crossref","award":["2024JJ739"],"award-info":[{"award-number":["2024JJ739"]}],"id":[{"id":"10.13039\/501100004761","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Research Foundation of the Education Bureau of Hunan Province, China","award":["22A0371"],"award-info":[{"award-number":["22A0371"]}]},{"name":"Hunan Student\u2019s Innovation and Entrepreneurship Training Program","award":["S202510531054"],"award-info":[{"award-number":["S202510531054"]}]},{"name":"Hunan Provincial Common Institutions of Higher Learning Teaching Reform Research Project","award":["HNJG-20230695"],"award-info":[{"award-number":["HNJG-20230695"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate segmentation of colorectal polyps is crucial for the early screening and clinical diagnosis of colorectal cancer. However, the diverse morphology of polyps, significant variations in scale, and unstable quality of endoscopic imaging pose serious challenges for existing algorithms in achieving precise boundary segmentation. To address these issues, this study proposes a novel polyp segmentation algorithm, GDCA-Net, which is developed based on the You Only Look Once version 12 segmentation model (YOLOv12-seg). GDCA-Net introduces several architectural innovations. First, a Gather-and-Distribute (GD) mechanism is incorporated to optimize multi-scale feature fusion, while Alterable Kernel Convolution (AKConv) is integrated to enhance the modeling of complex geometric structures. Second, the Convolution and Attention Fusion Module (CAF) and Context-Mixing dynamic convolution (ContMix) modules are designed to strengthen long-range dependency modeling and multi-scale feature extraction for polyp boundary representation. Finally, a Wise Intersection over Union\u2013based (Wise-IoU) loss function is introduced to accelerate model convergence and improve robustness to low-quality samples. Experiments conducted on the PolypDB, Kvasir-SEG, and CVC-ClinicDB datasets demonstrate the superior performance of GDCA-Net in polyp segmentation tasks. On the most challenging PolypDB dataset, GDCA-Net achieved a mean Average Precision at 50% IoU threshold (mAP50) of 85.9% and an F1-score (F1) of 85.5%, representing improvements of 2.2% and 0.7% over YOLOv12-seg, respectively. Moreover, on the Kvasir-SEG dataset, GDCA-Net achieved a leading F1 score of 94.9%. These results clearly demonstrate that GDCA-Net possesses strong performance and generalization capabilities in handling polyps of varying sizes, shapes, and imaging qualities.<\/jats:p>","DOI":"10.3390\/s25206495","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T14:53:19Z","timestamp":1761058399000},"page":"6495","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Research on Polyp Segmentation via Dynamic Multi-Scale Feature Fusion and Global\u2013Local Semantic Enhancement"],"prefix":"10.3390","volume":"25","author":[{"given":"Wei","family":"Qing","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Jishou University, Jishou 416000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyao","family":"Ouyang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Jishou University, Jishou 416000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengfei","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Jishou University, Jishou 416000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"128767","DOI":"10.1016\/j.neucom.2024.128767","article-title":"A survey of deep learning algorithms for colorectal polyp segmentation","volume":"614","author":"Li","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rana, D., Pratik, S., Balabantaray, B.K., Peesapati, R., and Pachori, R.B. 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