Exploring Vision Transformer Architectures for Brain Tumor Classification: A Comprehensive Study

Automated brain tumor classification has garnered considerable attention as a research area, with Convolutional Neural Networks (CNN) and deep learning emerging as the standard tools for developing cutting-edge solutions. In this paper, we propose a Vision Transformer (VIT) architecture and examine their efficacy and limitations. Our evaluation process encompasses various parameter configurations, including different image size, transformer block number, and patch size. We conduct training and testing on a publicly available brain tumor classification dataset containing 3064 images across three tumor classes: meningioma, glioma, and pituitary tumor. Through rigorous evaluation, we find that our proposed VIT model can achieve competitive performance compared our previous CNN model. The best results include an overall Dice similarity score and correct decision rate of 98.9%, with AUC values exceeding 99.6% for each tumor class.