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To address these issues, we propose LKCAU-Net, a novel hybrid multi-scale network that integrates Large Kernel Coordinate Attention (LKCA) and Atrous Spatial Pyramid Pooling (ASPP). The LKCA module enhances the model\u2019s ability to capture spatial details and global contextual information, while the ASPP module enhances multi-scale contextual representation. Embedded within a deep U-Net architecture, these components enable effective global-local feature fusion, significantly improving the robustness and accuracy of breast tumor segmentation in complex ultrasound images. We conducted experiments on four widely used public datasets: BUSI, Dataset B, BUSBRA and QAMEBI. For Dataset B, our proposed LKCAU-Net attained a Dice score of 0.8215, a Jaccard index of 0.7167, a precision of 0.8797, a recall of 0.7937, and a specificity of 0.9949. On the BUSI dataset, it reached a Dice score of 0.7973, Jaccard index of 0.6899, precision of 0.8320, recall of 0.7886, and specificity of 0.9825. When evaluated on the BUSBRA dataset, the model achieved a Dice score of 0.9085, Jaccard index of 0.8402, precision of 0.9206, recall of 0.9057, and specificity of 0.9964. On the QAMEBI, it reached a Dice score of 0.8815, Jaccard index of 0.8770, precision of 0.8993, recall of 0.8937, and specificity of 0.9949. Experimental results demonstrate that LKCAU-Net outperforms current state-of-the-art segmentation approaches, providing enhanced accuracy and robustness in breast cancer segmentation from ultrasound images.<\/jats:p>","DOI":"10.1177\/01617346261463796","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T07:12:36Z","timestamp":1783321956000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["LKCAU-Net: A Large Kernel Coordinated Attention U-Net for Breast Tumors Segmentation in Ultrasound Images"],"prefix":"10.1177","author":[{"given":"Lai","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Media and Design, Hangzhou Dianzi University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"YuXin","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Media and Design, Hangzhou Dianzi University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7012-7192","authenticated-orcid":false,"given":"Aibin","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Media and Design, Hangzhou Dianzi University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,6]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-025-03502-3"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3178991","article-title":"DS-TransUNet: dual swin transformer U-Net for medical image segmentation","volume":"71","author":"Lin A","year":"2022","unstructured":"Lin A, Chen B, Xu J, Zhang Z, Lu G, Zhang D. 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