PMCA ‐ UNet : A Parameter‐Efficient Skin Lesion Segmentation Network With Initial Feature Preservation and Parallel Multi‐Scale Convolutional Attention

Skin lesion segmentation is a critical component of computer‐aided dermoscopic image analysis, and its accuracy directly affects subsequent evaluation reliability. However, small lesions, ambiguous boundaries, and foreground–background imbalance remain challenging, while high‐performance methods often involve large model sizes that limit deployment in resource‐constrained scenarios. To address these issues, this paper proposes PMCA‐UNet, a parameter‐efficient skin lesion segmentation network based on AMSUnet. The proposed model introduces an enhanced initial feature preservation module (E‐PFC) to strengthen shallow texture and boundary information, and a parallel multi‐scale convolutional attention module (P‐MCA) to improve contextual representation for lesions with different scales. In addition, the CE‐Dice hybrid loss weighting strategy is optimized to alleviate lesion–background pixel imbalance. On the ISIC2018 dataset, PMCA‐UNet achieves Dice and IoU values of 0.9184 and 0.8642, improving the AMSUnet baseline by 3.47 and 5.47 percentage points, respectively. Its sensitivity reaches 0.9478, indicating improved lesion recall capability. The model also achieves Dice scores of 0.9256 and 0.9408 on the ISIC2016 and PH2 datasets, respectively. With only 3.186 M parameters, PMCA‐UNet demonstrates a favorable balance between segmentation performance and model efficiency. The source code is available at: https://github.com/hang‐hang683/PMCA‐Seg .