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However, the task faces challenges such as limited labeled data, data imbalance, and high intra\u2010class variability. In this paper, we propose a lightweight local\u2013global fusion (LGF) model that leverages the advantages of RegNet for local processing and Transformer for global interaction. The LGF model consists of four stages that integrate local and global pathological information using channel attention and residual connections. Furthermore, Polyloss is employed to address the data imbalance. Extensive experiments on the ISIC2018 and ISIC2019 datasets demonstrate that LGF achieves state\u2010of\u2010the\u2010art performance with 93.10% and 90.36% accuracy, respectively, without any data augmentation. The LGF model is relatively lightweight and easier to reproduce, contributing to the field by offering a satisfactory trade\u2010off between model complexity and classification performance. The code for our model will be available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/candiceyyy\/LGF\">https:\/\/github.com\/candiceyyy\/LGF<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1002\/ima.70045","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T21:15:11Z","timestamp":1741727711000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Lightweight Local\u2013Global Fusion for Robust Multiclass Classification of Skin Lesions"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3068-2869","authenticated-orcid":false,"given":"Guangli","family":"Li","sequence":"first","affiliation":[{"name":"School of Information and Software Engineering East China Jiaotong University  Nanchang China"}]},{"given":"Xinjiong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering East China Jiaotong University  Nanchang China"}]},{"given":"Yiyuan","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering East China Jiaotong University  Nanchang China"}]},{"given":"Jingqin","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering East China Jiaotong University  Nanchang China"}]},{"given":"Donghong","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering Wuhan University  Wuhan China"}]},{"given":"Jianguo","family":"Wu","sequence":"additional","affiliation":[{"name":"The Second Affiliated Hospital of Nanchang University  Nanchang China"}]},{"given":"Ruiyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hainan International College Communication University of China  Beijing China"}]},{"given":"Hongbin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering East China Jiaotong University  Nanchang China"}]}],"member":"311","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"e_1_2_12_2_1","unstructured":"WHO \u201cSkin Cancers \u201d(2017) accessed June 19 2019 https:\/\/www.who.int\/uv\/faq\/skincancer\/en\/index1.html."},{"key":"e_1_2_12_3_1","first-page":"1","volume-title":"Cancer Facts Fig","author":"ACS","year":"2018"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.1365\u20102133.1998.02075.x"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.06.018"},{"key":"e_1_2_12_6_1","first-page":"1","article-title":"Skin Lesion Classification Using Decision Trees and Random Forest Algorithms","volume":"15","author":"Dhivyaa C. 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