LUT-SLS: A Lightweight Transformer Network Based on U-Net for Skin Lesion Segmentation

Abstract

In recent years, deep learning has achieved significant advancements in medical image segmentation, primarily focusing on CNN structures and Transformer-based architectures. However, these network architectures often suffer from high computational complexity and a large number of parameters. To address these challenges, this paper proposes a lightweight, novel structured network called LUT-SLS, based on U-Net and Transformer. Firstly, the overall structure integrates U-Net and Transformer, which effectively captures long-range dependencies in image relationships and contextual information, thereby improving segmentation accuracy. Secondly, a novel PLTS module is designed, which replaces the traditional self-attention mechanism with average pooling operations to extract global features and local details. Additionally, a novel MMLP structure is introduced, incorporating residual depth-separable operations into the traditional fully-connected framework. This enhances the processing of pooled features and further improves feature expression capability. Finally, the encoder and decoder parts are connected by the MSBN module, which facilitates the extraction of deep features while fusing encoder features. Experimental results demonstrate that the proposed model achieves competitive advantages in balancing the number of parameters, computational complexity, and performance compared to current leading models on multiple public datasets. This solution enables model deployment on IoT terminals, assisting doctors in making more accurate clinical decisions.