Advanced Brain Tumor Classification from MRI Images with Vision and Swin Transformer Models

Brain tumors, characterized by abnormal cell growth within the brain, represent one of the most life-threatening conditions due to their potential to severely impair brain function and cause neurological complications, including death. Accurate classification of brain tumors is crucial in medical diagnosis, as misdiagnosis can lead to ineffective treatment and reduced patient survival. Magnetic Resonance Imaging (MRI) is a widely utilized non-invasive technique for acquiring high-contrast grayscale brain images commonly employed in tumor diagnosis. This study uses MRI images to evaluate the effectiveness of two pre-trained transformer-based models for brain tumour classification, specifically the Vision Transformer and Swin Transformer. The models were trained and tested on a publicly available Kaggle dataset containing 3,000 brain MRI scans. Our methodology employed the fine-tuned Vision transformer and Swin Transformer on the MRI dataset for classification. The models' performance was assessed using accuracy, precision, recall, and F1-score metrics. The Vision Transformer achieved an accuracy of 95.30%, while the Swin Transformer outperformed it with an accuracy of 98.71%. These results highlight the efficacy of transformer models in accurately identifying common brain tumors, demonstrating their potential to enhance computer-aided diagnosis and support healthcare professionals in making swift and accurate diagnostic decisions.