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However, CNNs have limitations in perceiving global features and more subtle features, which makes existing methods unable to achieve ideal accuracy in tasks such as pneumoconiosis screening. To overcome these limitations, we propose MBSM\u2010Net, a new multi\u2010branch structure\u2010enhanced model for pneumoconiosis screening and grading based on X\u2010ray images. MBSM\u2010Net introduces an adaptive feature selection and fusion module to achieve synchronous extraction and hierarchical fusion of global and local features. In the local feature extraction module, we designed a CNN\u2010Mamba module. This module integrates prior information through a detailed enhancement module to compensate for the shortcomings of traditional convolutions and significantly enhances the expression of subtle lesion information. Meanwhile, the Mamba module simulates pixel\u2010level long\u2010range dependencies to extract finer\u2010grained texture features. In the global feature extraction module, we cleverly utilize the windowed multi\u2010head self\u2010attention (W\u2010MSA) mechanism, enabling the model to better understand the overall distribution and degree of fibrosis of pulmonary lesions. We validated the MBSM\u2010Net model on 1,760 real anonymized patient X\u2010ray chest films. The results showed that the accuracy of the MBSM\u2010Net model reached 78.6%, and the\n                    <jats:italic>F<\/jats:italic>\n                    1 score reached 79%, both of which are superior to existing models.\n                  <\/jats:p>","DOI":"10.1049\/ipr2.70128","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T03:26:01Z","timestamp":1752722761000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MBSM\u2010Net: A Multi\u2010Branch Structure Model for Pneumoconiosis Screening and Grading of Chest X\u2010Ray Images"],"prefix":"10.1049","volume":"19","author":[{"given":"Shuzhi","family":"Su","sequence":"first","affiliation":[{"name":"Joint Research Center for Occupational Medicine and Health of IHM Anhui University of Science &amp; Technology  Huainan China"},{"name":"The First Hospital Anhui University of Science &amp; Technology  Huainan China"},{"name":"School of Computer Science and Engineering Anhui University of Science &amp; Technology  Huai\u2010nan Anhui China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1869-6611","authenticated-orcid":false,"given":"Yifan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Anhui University of Science &amp; Technology  Huai\u2010nan Anhui China"}]},{"given":"Yanmin","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering Anhui University of Science &amp; Technology  Huainan Anhui China"}]},{"given":"Yong","family":"Dai","sequence":"additional","affiliation":[{"name":"The First Hospital Anhui University of Science &amp; Technology  Huainan China"}]},{"given":"Zekuan","family":"Yu","sequence":"additional","affiliation":[{"name":"Academy for Engineering and Technology Fudan University  Shanghai China"}]},{"given":"Zhi\u2010Ri","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Science and Engineering Jinan University  Zhuhai China"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"China Coal (Tianjin) Underground Engineering Intelligent Research Institute Co., Ltd.  Tianjin China"}]},{"given":"Shengzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"China Coal (Tianjin) Underground Engineering Intelligent Research Institute Co., Ltd.  Tianjin China"}]}],"member":"265","published-online":{"date-parts":[[2025,7,16]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2024.02.057"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108516"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00868-x"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107660"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2024.107457"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116288"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-021-00723-z"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-024-01729-0"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108372"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102917"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120710"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105741"},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","unstructured":"J.Ruan J.Li andS.Xiang \u201cVM\u2010Unet: Vision Mamba Unet for Medical Image Segmentation \u201d preprint arXiv:2402.02491 November 8 2024 https:\/\/doi.org\/10.48550\/arXiv.2402.02491.","DOI":"10.1145\/3767748"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.129580"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103334"},{"key":"e_1_2_9_17_1","unstructured":"Y.YueandZ.Li \u201cMedMamba: Vision Mamba for Medical Image Classification \u201d preprint arXiv:2403.03849 September 29 2024 https:\/\/doi.org\/10.48550\/arXiv.2403.03849."},{"key":"e_1_2_9_18_1","unstructured":"A.GuandT.Dao \u201cMamba: Linear\u2010Time Sequence Modeling With Selective State Spaces \u201d preprint arXiv:2312.00752 December 1 2023 https:\/\/doi.org\/10.48550\/arXiv.2312.00752."},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinimag.2023.02.010"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3354108"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11760-022-02388-9"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.14445\/22315373\/IJMTT-V69I5P506"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103310"},{"key":"e_1_2_9_24_1","doi-asserted-by":"crossref","unstructured":"R.Wu Y.Liu P.Liang andQ.Chang \u201cH\u2010vmunet: High\u2010Order Vision Mamba Unet for Medical Images Segmentation \u201d preprint arxiv:2403.13642 March 20 2024 https:\/\/doi.org\/10.1016\/j.neucom.2025.129447.","DOI":"10.1016\/j.neucom.2025.129447"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2024.3490732"},{"key":"e_1_2_9_26_1","doi-asserted-by":"crossref","unstructured":"J.Li Y.Wen andL.He \u201cSCConv: Spatial and Channel Reconstruction Convolution for Feature Redundancy \u201d inProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(IEEE 2023) 6153\u20136162.","DOI":"10.1109\/CVPR52729.2023.00596"},{"key":"e_1_2_9_27_1","doi-asserted-by":"crossref","unstructured":"Z.Liu Y.Lin Y.Cao et\u00a0al. \u201cSwin Transformer: Hierarchical Vision Transformer Using Shifted Windows \u201d inProceedings of the IEEE\/CVF International Conference on Computer Vision(IEEE 2021) 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105534"},{"key":"e_1_2_9_29_1","unstructured":"Y.Liu Z.Shao andN.Hoffmann \u201cGlobal Attention Mechanism: Retain Information to Enhance Channel\u2010Spatial Interactions \u201d preprint arxiv:2112.05561 December 10 2021 https:\/\/doi.org\/10.48550\/arXiv.2112.05561."},{"key":"e_1_2_9_30_1","unstructured":"X.Zhang C.Liu D.Yang et\u00a0al. \u201cRFAConv: Innovating Sspatial Attention and Standard Convolutional Operation \u201d preprint arXiv:2304.03198 October 12 2023 https:\/\/arxiv.org\/html\/2304.03198v6."},{"key":"e_1_2_9_31_1","doi-asserted-by":"crossref","unstructured":"Q.Wu T.Yang Z.Liu B.Wu Y.Shan andA. 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