  Skin lesion segmentation is key for early skin cancer detection. Challenges
in automatic segmentation from dermoscopic images include variations in color,
texture, and artifacts of indistinct lesion boundaries. Deep learning methods
like CNNs and U-Net have shown promise in addressing these issues. To further
aid early diagnosis, especially on mobile devices with limited computing power,
we present MUCM-Net. This efficient model combines Mamba State-Space Models
with our UCM-Net architecture for improved feature learning and segmentation.
MUCM-Net's Mamba-UCM Layer is optimized for mobile deployment, offering high
accuracy with low computational needs. Tested on ISIC datasets, it outperforms
other methods in accuracy and computational efficiency, making it a scalable
tool for early detection in settings with limited resources. Our MUCM-Net
source code is available for research and collaboration, supporting advances in
mobile health diagnostics and the fight against skin cancer. In order to
facilitate accessibility and further research in the field, the MUCM-Net source
code is https://github.com/chunyuyuan/MUCM-Net
