MUCM-Net: A Mamba Powered UCM-Net for Skin Lesion Segmentation
It addresses early skin cancer detection in resource-limited settings like mobile devices, but is incremental as it builds on existing deep learning methods.
The paper tackles skin lesion segmentation from dermoscopic images by proposing MUCM-Net, an efficient model combining Mamba State-Space Models with a UCM-Net architecture, which outperforms other methods in accuracy and computational efficiency on ISIC datasets.
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