Combining Local and Global Feature Extraction for Brain Tumor Classification: A Vision Transformer and iResNet Hybrid Model

Early diagnosis of brain tumors is crucial for effective treatment and patient prognosis. Traditional Convolutional Neural Networks (CNNs) have shown promise in medical imaging but have limitations in capturing long-range dependencies and contextual information. Vision Transformers (ViTs) address these limitations by leveraging self-attention mechanisms to capture both local and global features. This study aims to enhance brain tumor classification by integrating an improved ResNet (iResNet) architecture with a ViT, creating a robust hybrid model that combines the local feature extraction capabilities of iResNet with the global feature extraction strengths of ViTs. This integration results in a significant improvement in classification accuracy, achieving an overall accuracy of 99.2%, outperforming established models such as InceptionV3, ResNet, and DenseNet. High precision, recall, and F1 scores were observed across all tumor classes, demonstrating the model's robustness and reliability. The significance of the proposed method lies in its ability to effectively capture both local and global features, leading to superior performance in brain tumor classification. This approach offers a powerful tool for clinical decision-making, improving early detection and treatment planning, ultimately contributing to better patient outcomes.