ArticleScientific reports2025
Sparse transformer and multipath decision tree: a novel approach for efficient brain tumor classification.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- NeXtSwin-X: dual-branch cross-attention fusion of ConvNeXt and swin transformer for accurate brain tumor classification from MRI and CT.Scientific reports · 2026Article
- Applications of artificial intelligence in mechanical engineering for the field of upper limb exoskeletons.Scientific reports · 2026Article
- An Efficient and Cohesive System for Enhanced Accuracy in Malignant Brain Tumor Diagnosis.Current medical imaging · 2026Article
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
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