PENG Jianqiang 1ZHANG Yufan2
作者信息
- 1. China Telecom Corporation Limited Sichuan Branch,Chengdu 610072,China
- 2. Beijing University of Posts and Telecommunications,Beijing 100876,China
- 折叠
摘要
Abstract
The incidence of skin diseases is rising globally,impacting patients'quality of life and potentially leading to mental health issues such as sleep disorders and depression.Existing models exhibit suboptimal performance in semantic segmentation of dermatological lesion imag-es.This paper therefore constructs the high-quality CliAD dataset,featuring precise annotations and highly realistic images containing numerous punctate annotations.Significant differences between dermatological categories present substantial challenges for lesion identification and seg-mentation.To address these challenges,the semantic segmentation model MVMNet is proposed.This model is based on Mamba and Convolutional Neural Network(CNN),utilizing VMM Blocks for fine-grained feature extraction to enhance recognition of punctate regions.To address inter-cat-egory differences,a U-shaped architecture is employed to fuse and extract features across different levels,enabling the identification of category-specific characteristics.Experimental results demonstrate the model's superior performance on the CliAD,ISIC17,and ISIC18 datasets.关键词
皮肤病/数据集/语义分割模型/Mamba/Convolutional Neural NetworkKey words
Dermatology/Dataset/Semantic segmentation model/Mamba/Convolutional Neural Network分类
信息技术与安全科学
