Abstract: Segmenting skin lesions is critical for early skin cancer detection. Existing CNN and Transformer-based methods face challenges such as high computational complexity and limited adaptability to variations in lesion sizes. To overcome these limitations, we introduce MSAMamba-UNet, a lightweight model that integrates two novel architectures: Multi-Scale Mamba (MSMamba) and Adaptive Dynamic Gating Block (ADGB). MSMamba utilizes multi-scale decomposition and a parallel hierarchical structure to enhance the delineation of irregular lesion boundaries and sensitivity to small targets. ADGB dynamically selects convolutional kernels with varying receptive fields based on input features, improving the model’s capacity to accommodate diverse lesion textures and scales. Additionally, we introduce a Mix Attention Fusion Block (MAF) to enhance shallow feature representation by integrating parallel channel and pixel attention mechanisms. Extensive evaluation of MSAMamba-UNet on the ISIC 2016, ISIC 2017, and ISIC 2018 datasets demonstrates competitive segmentation accuracy with only 0.056 M parameters and 0.069 GFLOPs. Our experiments revealed that MSAMamba-UNet achieved IoU scores of 85.53%, 85.47%, and 82.22%, as well as DSC scores of 92.20%, 92.17%, and 90.24%, respectively. These results underscore the lightweight design and effectiveness of MSAMamba-UNet.

Key words: Transformer, Segmenting skin lesions, Mamba, Lightweight model, Multi-scale')">Transformer, Segmenting skin lesions, Mamba, Lightweight model, Multi-scale