HMP-MUNet: A Hybrid Deep Learning Framework for Automated Skin Lesion Segmentation in Dermoscopic Images

Computer-aided diagnosis systems for cutaneous lesions face significant challenges in achieving both high diagnostic accuracy and computational efficiency. Current deep learning approaches often require substantial computational resources while struggling to capture the complex morphological variations inherent in skin lesions across different scales. High-order Multi-scale Parallel Vision Mamba U-Net (HMP-MUNet), a novel deep learning framework that addresses these limitations through an innovative architectural design combining State-Space Models with advanced multi-scale processing capabilities. The approach described in this study integrates a hybrid U-Net framework that combines a high-order vision state-space module for global context modeling, a multi-scale dilated attention fusion network for hierarchical feature extraction across multiple receptive fields, and a parallel multi-depth flexible network for computational optimization. This architecture uses a modified U-shaped encoder-decoder with increased channel dimensions and advanced attention mechanisms to enhance feature learning. Comprehensive evaluation on benchmark datasets shows a Dice Similarity Coefficient of 95.85% on PH2 and 90.44% on ISIC2018. The model accomplishes this superior accuracy using only 7.61M parameters, representing a remarkable 72.2% reduction compared to existing Vision Mamba architectures while maintaining high segmentation accuracy. The lightweight design shows potential for deployment across various clinical settings and imaging modalities, pending clinical validation, from specialized dermatology centers to primary care facilities. HMP-MUNet provides an automated solution for skin lesion segmentation that improves diagnostic consistency and supports clinical workflows, with potential for further validation in clinical use. The integration of high-order modeling capabilities with practical deployment considerations offers a potential solution for improving skin cancer screening protocols and expanding healthcare accessibility in computer-aided diagnosis applications.