Enhanced Disease Classification with HybridCNNViT model and Synthetic Oversampling (SMOTE)
Early and accurate classification of skin diseases plays a crucial role in improving diagnostic efficiency and patient outcomes. This thesis introduces HybridCNNViT, a deep learning algorithm that combines a setup of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to capture both local texture and global structure from skin lesion images. To address the common issue of class im
