Hybrid Wavelet-Swin Transformer and Dual-Path Attention Networks for Robust Skin Lesion Segmentation

Hybrid Wavelet-Swin Transformer and Dual-Path Attention Networks for Robust Skin Lesion Segmentation project presents an advanced deep learning framework for accurate skin lesion segmentation using dermoscopic images from the ISIC 2017 dataset. The proposed system aims to improve lesion boundary detection and feature extraction by combining convolutional and transformer-based architectures. Two segmentation models are developed and evaluated: WSB-Net, which integrates Wavelet Transform, Swin Transformer, and Boundary Refinement modules to capture fine-grained texture details and precise lesion boundaries, and DCT-Net, which combines a Dual-Path CNN, Transformer encoder, and Cross-Attention mechanism to learn both local and global image representations. A Flask-based web application is implemented to provide an interactive platform where users can register, log in, upload skin lesion images, and obtain segmented outputs. By leveraging complementary feature learning strategies, the framework enhances segmentation accuracy, boundary precision, and overall model robustness. The project demonstrates the effectiveness of hybrid deep learning techniques for skin lesion analysis and provides a practical solution for automated lesion segmentation.