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This paper proposes TCAFNet, a lightweight deep learning framework that integrates multi\u2010scale attention mechanisms and transformer\u2010based refinement to enhance both local feature discrimination and global contextual reasoning. The proposed model is built upon a pre\u2010trained EfficientNetB0 backbone and employs an early semantic fusion strategy, in which deep features are injected into intermediate layers to guide feature learning from early stages. Cross\u2010scale dependencies are explicitly modelled using pairwise cross\u2010attention modules operating on multi\u2010level feature maps. Each attention\u2010enhanced representation is further refined using a customized adaptive convolutional block attention module and an adaptive feature pyramid attention block. The refined features are then integrated through an attention\u2010based fusion mechanism and processed by a lightweight Transformer block to capture long\u2010range spatial relationships. Experimental results show that the proposed model achieves an accuracy of 99.35% on the test set of the Figshare dataset, and demonstrates strong cross\u2010dataset robustness with accuracies of 99.29% and 99.67% on the test sets of Nickparvar and Br35H datasets, respectively. These results demonstrate the effectiveness of combining early feature guidance, cross\u2010scale attention and transformer\u2010based modelling for robust multi\u2010class brain tumour classification.<\/jats:p>","DOI":"10.1049\/ipr2.70303","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T22:16:54Z","timestamp":1773008214000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TCAFNet: Transformer\u2010Guided Cross\u2010Scale Attention and Deep Semantic Fusion for Brain Tumour Classification From MRI"],"prefix":"10.1049","volume":"20","author":[{"given":"Maedeh","family":"Mohammadi","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering Faculty of Engineering Golestan University  Gorgan Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4560-4759","authenticated-orcid":false,"given":"Majid","family":"Ziaratban","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Faculty of Engineering Golestan University  Gorgan Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.zemedi.2018.11.002"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15026"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2019.05.001"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/ima.70128"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106117"},{"key":"e_1_2_10_8_1","unstructured":"M.TanandQ. 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