SFUNet : A Skin Lesion Segmentation Method Based on Fuzzy Learning

ABSTRACT

In recent years, segmentation models based on deep learning have provided new perspectives and methods for automated skin lesion diagnosis. However, when dealing with dermoscopic images, existing methods often ignore uncertain lesion boundaries and ambiguous regions, leading to limited segmentation stability and accuracy. To solve this problem, this paper proposes a novel network for dermoscopic image segmentation based on fuzzy learning, named SFUNet. By integrating the Transformer backbone into the U‐Net architecture, the model extracts semantic information from input medical images and yields a pixel‐level representation for segmentation. Since boundary uncertainty and region ambiguity are widespread in dermoscopic image segmentation, a new adaptive fuzzy module is integrated into the U‐Net skip connection path. This module constructs several membership functions for the input multi‐scale features to characterize gradual transitions at uncertain lesion boundaries, and applies an adaptive gated fusion mechanism to the reconstructed features, thereby improving boundary accuracy and suppressing noise. In addition, an attention mechanism is introduced in the decoder to focus on the lesion‐related regions. The effectiveness of SFUNet was evaluated through comparisons with existing advanced segmentation networks on the ISIC 2017 and ISIC 2018 datasets. Cross‐dataset evaluation was performed on the PH dataset to verify the model's generalization ability. Experimental results showed that SFUNet improved overlap and boundary metrics, demonstrating its strong segmentation performance and computational efficiency.