An Exploratory Approach Towards Investigating and Explaining Vision Transformer and Transfer Learning for Brain Disease Detection

AI models can now match expert-level accuracy in brain disease detection: The Vision Transformer model achieved 94.39% accuracy in classifying brain diseases from MRI scans, demonstrating that artificial intelligence has reached a level of performance that could meaningfully assist medical professionals in diagnosis.

Explainability is essential for medical AI adoption: The study employed five different explainability methods (GradCAM, GradCAM++, LayerCAM, ScoreCAM, and Faster-ScoreCAM) to show doctors why the AI made specific predictions, highlighting that transparency—not just accuracy—is critical for building trust in clinical settings.

Newer AI architectures (Vision Transformers) outperform older approaches: Vision Transformers surpassed traditional transfer learning models like VGG16, VGG19, ResNet50V2, and MobileNetV2, suggesting that continued investment in advancing AI technology can yield meaningful improvements in medical imaging analysis.

Transfer learning helps overcome data scarcity challenges: By fine-tuning pre-trained models rather than training from scratch, the researchers were able to work effectively with a Bangladesh-based dataset, demonstrating a practical approach to developing AI tools for healthcare systems with limited data resources.

Responsible AI requires both performance and interpretability: The study's emphasis on combining high accuracy with explainability methods reflects best practices in responsible AI—ensuring that powerful medical tools are not just effective, but also understandable and trustworthy to the clinicians who use them.