Li Pengcheng, Jin Yuqi, Wang Monan, Liu Fengjie
Key Laboratory of Advanced Manufacturing and Intelligent Technology (Ministry of Education), Harbin University of Science and Technology, Heilongjiang, China.
College of Electronic and Electrical Engineering, Henan Normal University, Xinxiang, China.
Sci Rep. 2025 Aug 7;15(1):28915. doi: 10.1038/s41598-025-13115-y.
Early classification of brain tumors is the key to effective treatment. With advances in medical imaging technology, automated classification algorithms face challenges due to tumor diversity. Although Swin Transformer is effective in handling high-resolution images, it encounters difficulties with small datasets and high computational complexity. This study introduces SparseSwinMDT, a novel model that combines sparse token representation with multipath decision trees. Experimental results show that SparseSwinMDT achieves an accuracy of 99.47% in brain tumor classification, significantly outperforming existing methods while reducing computation time, making it particularly suitable for resource-constrained medical environments.
脑肿瘤的早期分类是有效治疗的关键。随着医学成像技术的进步，由于肿瘤的多样性，自动分类算法面临挑战。尽管Swin Transformer在处理高分辨率图像方面很有效，但它在面对小数据集和高计算复杂性时遇到困难。本研究引入了SparseSwinMDT，这是一种将稀疏令牌表示与多路径决策树相结合的新型模型。实验结果表明，SparseSwinMDT在脑肿瘤分类中达到了99.47%的准确率，显著优于现有方法，同时减少了计算时间，使其特别适用于资源受限的医疗环境。
