Vision Transformer with CLAHE preprocessing, two-stage fine-tuning, MixUp/CutMix, EMA, TTA, and attention rollout achieves 99.29% accuracy and 99.25% macro F1 on four-class brain tumor MRI classification from 7023 scans.
Hybrid architectures like TransUNet (Chen et al., 2021) c ombined CNN encoders with Transformer reasoning, achieving state -of- the-art on multi-organ CT segmentation
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an interpretable vision transformer framework for automated brain tumor classification
Vision Transformer with CLAHE preprocessing, two-stage fine-tuning, MixUp/CutMix, EMA, TTA, and attention rollout achieves 99.29% accuracy and 99.25% macro F1 on four-class brain tumor MRI classification from 7023 scans.