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.
Researchers extracted texture features (Gray -Level Co -occurrence Matrix, Gabor fil ters), morphological descriptors, and intensity statistics from segmented tumor regions
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.