DSEUNet: A lightweight UNet for dynamic space grouping enhancement for skin lesion segmentation

A prevalent cancer, skin cancer, requires precise segmentation for effective diagnosis and treatment. Despite the availability of numerous effective, lightweight neural network models, there remains a significant gap in their application in resource-limited settings, particularly in mobile healthcare. This study aims to bridge this gap by developing a model combining high efficiency and robust performance in such environments. This study introduces the Dynamic Spatial Group Enhanced Network (DSEUNet), significantly reducing the computational loads and parameter quantities while ensuring competitive segmentation accuracy. Our essential contribution is developing a model that excels in segmentation accuracy and addresses computational and parameter efficiency challenges in mobile healthcare applications. In extensive trials on the ISIC2017 and ISIC2018 datasets, DSEUNet not only surpasses traditional models like UNet in accuracy but does so with far fewer parameters and reduced computational demand. This research contributes to developing more accessible and efficient tools for medical image analysis, particularly in resource-constrained environments. As far as we know, this is the first model with 23.306 KB parameters and 9.497 M FLOPs.