通信与信息技术Issue(2)：11-15,5.
基于ESS-Net模型的皮肤病变分割算法研究
Research on skin lesion segmentation algorithm based on ESS-Net model
摘要
Abstract
The current skin lesion segmentation algorithms face challenges in accurately segmenting multi-dimensional and multi-scale skin lesions.To address this issue,we propose ESS-Net,an improved skin lesion segmentation model based on EGE-UNet.Our al-gorithm combines the advantages of both multi-scale attention and multi-dimensional attention.Specifically,we introduce an efficient multi-scale attention module in the encoder to capture lesions at different scales,and incorporate a parameter-free multi-dimensional at-tention module after the original bridge layer to comprehensively extract features across channel and spatial dimensions.Furthermore,we add a spatial-channel reconstruction convolution module after the convolutional layers in the decoder to enable multi-dimensional recon-struction and reduce redundancy.Experimental results on the ISIC 2017 dataset demonstrate that ESS-Net achieves superior perfor-mance with mean Intersection over Union(mIoU)of 80.04%and Dice Similarity Coefficient(DSC)of 88.89%.Comparative experiments with other models show that ESS-Net outperforms existing approaches,while ablation studies confirm the effectiveness of the improve-ments made to the baseline model.关键词
皮肤病变分割/深度学习/注意力机制/卷积模块Key words
Skin lesion segmentation/Deep learning/Attention mechanism/Convolutional module分类
信息技术与安全科学引用本文复制引用
翟丁婕,朱立忠..基于ESS-Net模型的皮肤病变分割算法研究[J].通信与信息技术,2026,(2):11-15,5.基金项目
国家重点研发计划(项目编号:2017YFC0821001-2) （项目编号:2017YFC0821001-2）
