Lightweight Medical Image Segmentation with UNet Architecture

This paper proposes two efficient lightweight segmentation models, MCS-Net and DAC-Net, based on a symmetric six-level U-shaped architecture that enhances feature representation with low computational cost. The key innovation is the integration of attention mechanisms and multi-scale contextual cues into the UNet framework for more discriminative feature learning and improved boundary delineation. Experimental results show improved DSC and mIoU in skin lesion segmentation, demonstrating the effectiveness of the proposed designs.