This project provides an interactive interface to classify MRI scans for Alzheimer's disease and brain tumors using transformer-based SegFormer models (nvidia/mit-b1). Each model is fine-tuned for image classification with four output classes specific to the condition. The models were trained on labeled MRI data using data augmentation techniques to improve generalization and reduce overfitting. Both models achieved an average validation accuracy of approximately 99.5%. The system is deployed using Gradio, enabling users to upload an MRI scan, select the condition type, and receive an instant prediction.