AC-MAMBASEG: An adaptive convolution and Mamba-based architecture for enhanced skin lesion segmentation

TL;DR

This paper introduces AC-MambaSeg, a novel skin lesion segmentation model combining hybrid CNN-Mamba backbone with attention modules, demonstrating improved accuracy on multiple datasets for early dermatological disease detection.

Contribution

The paper presents a new hybrid CNN-Mamba architecture with attention mechanisms for enhanced skin lesion segmentation, outperforming existing methods.

Findings

Improved segmentation accuracy on ISIC-2018 and PH2 datasets.

Effective focus on informative regions with attention modules.

Potential for aiding early dermatological diagnosis.

Abstract

Skin lesion segmentation is a critical task in computer-aided diagnosis systems for dermatological diseases. Accurate segmentation of skin lesions from medical images is essential for early detection, diagnosis, and treatment planning. In this paper, we propose a new model for skin lesion segmentation namely AC-MambaSeg, an enhanced model that has the hybrid CNN-Mamba backbone, and integrates advanced components such as Convolutional Block Attention Module (CBAM), Attention Gate, and Selective Kernel Bottleneck. AC-MambaSeg leverages the Vision Mamba framework for efficient feature extraction, while CBAM and Selective Kernel Bottleneck enhance its ability to focus on informative regions and suppress background noise. We evaluate the performance of AC-MambaSeg on diverse datasets of skin lesion images including ISIC-2018 and PH2; then compare it against existing segmentation methods. Our model shows promising potential for improving computer-aided diagnosis systems and facilitating early detection and treatment of dermatological diseases. Our source code will be made available at: https://github.com/vietthanh2710/AC-MambaSeg.