This project explores how different deep learning architectures can be used for medical image classification (e.g., brain tumour detection). We will compare traditional CNNs with state-of-the-art models such as Vision Transformers, Variational Autoencoders, and/or leading pre-trained models. Our goal is to understand the trade-offs between these methods (e.g., accuracy, computational efficiency).