Integrating Hypervision Transformer for Robust Brain Tumor Classification Using Deep Learning

Electronic health records, imaging data, and genetic data are the most common forms of medical data, and imaging data makes up a substantial majority of them. Accurate and timely diagnosis of brain tumors remains a huge challenge because of the heterogeneity of tumors and the need for highaccuracy classification. This study suggests a new deep learning paradigm using HyperVision Transformer for automated brain tumor classification with the limitation of conventional convolutional and transformer-based approaches. The model is also trained on the same balanced dataset with four categories of brain tumors: glioma, meningioma, no tumor, and pituitary tumor. Our architecture blends convolutional layers for initial feature extraction and hierarchical processing with transformerinspired dense layers for intricate spatial relationships in medical images. Experimental results indicate that our model outperforms traditional CNN-based architectures and baseline Vision Transformers, with an excellent training accuracy of 99.74 % and test accuracy of 95.88 % and enhanced generalization capabilities. Comparison with the best available models demonstrates the strength and effectiveness of our method in medical image classification. The results show that HyperVision Transformer has the potential to be an effective and scalable tool for detecting brain tumors, which holds the potential for new perspectives for better diagnostic solutions in healthcare.