Papers › AC-MAMBASEG: An adaptive convolution and Mamba-based architecture for enhanced skin...

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

5 May 2024arXiv:2405.03011archive 2025-07-28

Viet-Thanh Nguyen, Van-Truong Pham, Thi-Thao Tran

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.

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Lesion Segmentation Mamba Segmentation Skin Lesion Segmentation

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1x1 Convolution Average Pooling Batch Normalization CBAM Convolution Dense Connections Dilated Convolution Focus Max Pooling ReLU Selective Kernel Selective Kernel Convolution Sigmoid Activation Softmax

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