TY  - JOUR
AU  - Pacal, Ishak
PY  - 2026
DA  - 2026/09/09
TI  - Modern Vision Transformer Models for Accurate and Explainable Brain Tumor MRI Classification
JO  - OBM Neurobiology
SP  - 352
VL  - 10
IS  - 03
AB  - Accurate brain tumor classification from magnetic resonance imaging (MRI) remains difficult because glioma, meningioma, and pituitary tumors may exhibit overlapping enhancement, morphology, and slice-dependent appearance. This study presents a controlled comparison of Vision Transformer, Data-efficient Image Transformer (DeiT), Swin Transformer, BEiT, and EVA-02 under a common image-level protocol. The public Epic and CSCR Hospital Dataset record describes 12,064 preprocessed T1-weighted contrast-enhanced MRI images and was evaluated according to its published partitioning scheme. The original test set was reserved for final evaluation, and a validation subset was drawn solely from the original training set. BEiT-Base produced the highest observed accuracy (0.9921; Wilson 95% confidence interval, 0.9877-0.9950), a macro F1-score of 0.9924, and a macro area under the receiver operating characteristic curve of 0.9995, at a profiled cost of 33.70 giga floating-point operations per image, equal to that of Vision Transformer-Base and DeiT-Base. Accuracy intervals overlapped across models, and the differences were interpreted descriptively. Gradient-weighted Class Activation Mapping showed activation near the visually apparent lesion in many cases, but peripheral and non-lesion responses were also observed. Because patient and examination identifiers are unavailable, the findings represent image-level benchmark performance and do not establish patient-level generalization.
SN  - 2573-4407
UR  - https://doi.org/10.21926/obm.neurobiol.2603352
DO  - 10.21926/obm.neurobiol.2603352
ID  - Pacal2026
ER  -