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At this stage, feature maps experience a sharp decline in spatial resolution due to continuous downsampling, resulting in significant loss of critical boundaries and structural details. Additionally, the local receptive fields of convolutions limit the effective modelling of global context. To address this core issue, we propose a novel enhanced segmentation network called FMTNet. FMTNet fundamentally enhances the expressive power of deep features by integrating an innovative composite enhancement module at the bottleneck of the U\u2010Net. This module consists of three synergistically working submodules: the Fourier spatial fusion module, which introduces a frequency\u2010domain perspective to compensate for and reconstruct high\u2010frequency structural information lost in the spatial domain; the hybrid mamba\u2013transformer module, which efficiently captures cross\u2010regional long\u2010range dependencies to establish global context and the multi\u2010scale context Aggregation module, which fuses features of different scales to adapt to objects of varying sizes. We conducted extensive experiments on multiple public multi\u2010modal datasets, including colonoscopy polyps, dermatoscopy lesions, breast ultrasound and dental X\u2010rays. The results demonstrate that FMTNet comprehensively outperforms SOTA methods across all key metrics, showcasing exceptional segmentation accuracy and generalisation capabilities. Our research study demonstrates that by synergistically enhancing deep features across three dimensions\u2014frequency, global, and multi\u2010scale\u2014FMTNet provides a general and efficient solution to address the bottleneck issues of U\u2010Net, significantly enhancing the accuracy and robustness of medical image segmentation. The source code and pre\u2010trained weights are available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/shiguiling0-has\/FMTNet\">https:\/\/github.com\/shiguiling0\u2010has\/FMTNet<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1049\/cit2.70141","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T09:47:56Z","timestamp":1778579276000},"page":"798-815","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["FMTNet: A Fourier\u2010Mamba\u2013Transformer Enhanced Network for Medical Image Segmentation"],"prefix":"10.1049","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7539-5970","authenticated-orcid":false,"given":"Shaoqiang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering Qingdao University of Technology  Qingdao Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0419-5719","authenticated-orcid":false,"given":"Guiling","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering Qingdao University of Technology  Qingdao Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3935-3201","authenticated-orcid":false,"given":"Yuanyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering Qingdao University of Technology  Qingdao Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6922-5986","authenticated-orcid":false,"given":"Sibo","family":"Qiao","sequence":"additional","affiliation":[{"name":"School of Software Tiangong University  Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1125-1744","authenticated-orcid":false,"given":"Yuchen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Science Qingdao University of Technology  Qingdao Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8228-7436","authenticated-orcid":false,"given":"Yifan","family":"Wang","sequence":"additional","affiliation":[{"name":"The Seventh Clinical College of Guangzhou University of Chinese Medicine  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7278-3097","authenticated-orcid":false,"given":"Yawu","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Medical Informational Engineering Shandong University of Traditional Chinese Medicine  Jinan Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0371-9646","authenticated-orcid":false,"given":"Xiaochun","family":"Cheng","sequence":"additional","affiliation":[{"name":"Computer Science Department Bay Campus, Swansea University  Swansea UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s44267\u2010024\u201000071\u2010w"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.125419"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103547"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.111028"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.4324\/9781003454700"},{"key":"e_1_2_9_7_1","unstructured":"O.Oktay J.Schlemper L. 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