MCM-UNet: Mamba convolutional mixing network for skin lesion image segmentation

Abstract

Dermatological lesion image segmentation is a critical step in clinical diagnosis. However, due to the complex morphology, blurred boundaries, and variable sizes of lesions, accurate segmentation remains a significant challenge in medical image processing. Traditional methods often struggle to simultaneously capture both the overall contour and local details of lesion regions, severely constraining the accuracy of computer-assisted diagnosis. To address this issue, we propose MCM-UNet. We carefully design modules at the network’s shallow, deep, and skip connection stages to enhance spatial detail extraction, global dependency modeling, and cross-layer feature fusion. Through innovative feature extraction and fusion strategies, we effectively tackle the complexity of skin lesion segmentation. Based on this architecture, our network significantly improves the accuracy and robustness of dermatological lesion segmentation with a lightweight model of only 0.6M parameters. Experimental results on PH2, ISIC2017, and ISIC2018 public datasets demonstrate outstanding segmentation capabilities, achieving superior performance compared to existing methods and providing a novel solution for precise skin lesion segmentation.