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

Summary

HMP-MUNet introduces a hybrid deep learning framework for automated skin lesion segmentation. By integrating State-Space Models with multi-scale attention mechanisms, it enhances segmentation accuracy while optimizing computational efficiency, offering a robust solution for skin cancer diagnosis in clinical settings.

Abstract

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.

Introduction

Computer-aided diagnosis (CAD) systems aim to improve medical imaging practices across clinical specialties, enhancing disease detection, diagnosis, and treatment planning approaches¹. In dermatology, the integration of artificial intelligence (AI) and advanced imaging techniques has emerged as a critical tool for improving diagnostic accuracy and clinical outcomes, particularly in early detection of skin cancer, which accounts for approximately 90% of all skin malignancies². The development of sophisticated algorithmic approaches for automated cutaneous lesion analysis contributes to the advancement of precision medicin....

Protocol

This research was performed in compliance with institutional guidelines for computational research and data processing. No human subjects or vertebrate animals were involved in this study.

Dataset preparation and preprocessing

Dataset collection and organization

Dermoscopic images were collected from the PH2, ISIC2018, and ISIC2017 datasets, with specific links provided in the Table of Materials. The PH2 dataset contains 200 high-resolution images (1000 × 1000 pixels), ISIC2018 includes 2,594 images with corresponding ....

Results

HMP-MUNet architecture design for medical image segmentation

The proposed HMP-MUNet architecture demonstrated exceptional performance in automated cutaneous lesion segmentation tasks. Figure 1 illustrates the complete network architecture, showcasing the hierarchical U-shaped encoder-decoder structure with progressive channel expansion from 8 to 256 channels. The integration of three specialized modules in alternating configuration proved highly e.......

Discussion

This study presents HMP-MUNet (High-order Multi-scale Parallel Vision Mamba U-Net), a novel deep learning framework that addresses fundamental challenges in computer-aided diagnosis (CAD) for cutaneous lesion segmentation through innovative integration of State-Space Models (SSMs) with advanced architectural components¹. The approach described here demonstrates that combining high-order feature interaction mechanisms with multi-scale attention and parallel processing can achieve superior diagnosti.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CUDA 11.8	NVIDIA Corporation, Santa Clara, CA, USA		Software
GPU (32 GB VRAM)	NVIDIA		Operating system
ISIC2017 Dataset	ISIC Challenge	https://challenge.isic-archive.com/data	Datasets
ISIC2018 Dataset	ISIC Challenge	https://challenge.isic-archive.com/data	Datasets
NVIDIA V100 GPU	NVIDIA Corporation, Santa Clara, CA, USA		Hardware
PH2 Dataset	Universidade Do Porto	https://www.fc.up.pt/addi/ph2	Datasets
Python 3.8	Python	RRID:SCR_018536	Software
PyTorch 1.13.0	PyTorch	RRID:SCR_018536	Software
Ubuntu 20.04	Canonical		Hardware