LMFUNet: A Lightweight Multi-fusion UNet Based on Spiking Neural Systems for Skin Lesion Segmentation

Skin lesion segmentation is critical in medical image processing, but the segmentation task faces numerous challenges due to the differences in size, color, shape, and texture of skin lesions between patients, as well as the blurring of the boundary between lesions and normal skin. While many models improve segmentation performance by introducing complex modules, their computational resource constraints make applications limited in a clinical setting. To cope with this problem, we propose a lightweight multi-fusion network (LMFUNet) with parameters of only 0.100M and GFLOPs of 0.106. LMFUNet uses an Efficient Multi-scale Feature Extraction block (EMFE) in deep stages, which uses grouping of features by convolution with different dilation rates to reduce model complexity and effectively capture multi-scale features. By using the Multi-level Feature Fusion module (MFF) in skip connections, different levels of information are combined step by step, realizing the first step of fusion of low-level details and high-level contextual information, which helps to accurately localize the lesion area. After MFF, we designed the Spatial-channel Fusion module (SCF). This module further optimizes the fusion of feature information output from MFF in terms of spatial and channel dimensions, significantly enhancing the recognition of lesion boundaries. We did in-depth tests on three public datasets that are typical of others: ISIC2017, ISIC2018 and PH2 datasets. LMFUNet demonstrates superior performance because it excels at segmentation and doesn’t require a lot of computing power.