Comparative Analysis between CNN and ViT using Brain MRI Dataset

CNNs and ViTs are thoroughly compared in this work in several respects: architectural variations, training efficiency, performance on benchmark datasets, and adaptation to several image processing tasks. We analyze their training and validation loss patterns in addition to evaluating both models using the Brain MRI dataset depending on important criteria like accuracy, precision, recall, and ROC-AUC. While the ViT model noted superior outcomes with 88.5% accuracy, the CNN model reveals an accuracy of 85.5%. Furthermore, we find that ViTs show better performance in capturing long-range relationships inside the images than CNNs are in extracting localized features. Our results highlight the advantages and drawbacks of every method, therefore clarifying their relevance in several computer vision environments. Particularly in medical imaging and complicated vision tasks requiring fine-grained feature recognition, this work intends to aid academics and practitioners in selecting the best appropriate model architecture for their particular use cases.