Evidence map›Paper›PMID 41345219›Full record

ArticleScientific reports2025

Lightweight UNet with multi-module synergy and dual-domain attention for precise skin lesion segmentation.

Changhua Chen, Ling Li, Bo Li, Haijun Li, Yanjie You, Wei Zhou, Yuyan Bin, Zhuo Wang, Jiayi Li, Chao Zhang

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Changhua ChenSchool of Management, Xihua University, Chengdu, Sichuan, 610039, China.
Ling LiSchool of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, 610039, China.
Bo LiSchool of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, 610039, China. libo@mail.xhu.edu.cn.
Haijun LiDepartment of Cancer Center, The Second People's Hospital of Neijiang, Neijiang City, Sichuan Province, 641000, China.
Yanjie YouDepartment of Gastroenterology, People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, 750002, China.
Wei ZhouSchool of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, 610039, China.
Yuyan BinSchool of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, 610039, China.
Zhuo WangSchool of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, 610039, China.
Jiayi LiSchool of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, 610039, China.
Chao ZhangIntelligent Policing Key Laboratory of Sichuan Province, Sichuan Police College, Luzhou, Sichuan, 646000, China. galoiszhang@163.com.

Funding

the Opening Project of Intelli- gent Policing Key Laboratory of Sichuan Province ZNJW2022KFMS004the Opening Project of Intelli- gent Policing Key Laboratory of Sichuan Province ZNJW2022KFQN002the Science and Technology Program of Sichuan Province, China 2023YFG0264
6 · The paper itself

Abstract

Skin cancer poses a significant threat to life, necessitating early detection. Skin lesion segmentation, a critical step in diagnosis, remains challenging due to variations in lesion size and edge blurring. Despite recent advancements in computational efficiency, edge detection accuracy remains a bottleneck. In this paper, we propose a lightweight UNet with multi-module synergy and dual-domain attention for precise skin lesion segmentation to address these issues. Our model combines the Swin Transformer (Swin-T) block, Multi-Axis External Weighting (MEWB), Group multi-axis Hadamard Product Attention (GHPA), and Group Aggregation Bridge (GAB) within a lightweight framework. Swin-T reduces complexity through parallel processing, MEWB incorporates frequency domain information for comprehensive feature capture, GHPA extracts pathological information from diverse perspectives, and GAB enhances multi-scale information extraction. On the ISIC2017 and ISIC2018 datasets, our model achieves mIoU and DSC scores of 81.22% and 89.64%, and 81.65% and 89.90%, respectively. These results demonstrate improved segmentation accuracy with low parameter count and computational cost, aiding physicians in diagnosis and treatment. https://github.com/SolitudeWolf/ESMDL-UNet.git.

Indexed as

Image Interpretation, Computer-AssistedImage Processing, Computer-AssistedSkinSkin NeoplasmsAlgorithmsHumansImage segmentationLightweight modelMedical imagingSkin lesion

Identifiers

PMID41345219
PMCPMC12738719

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.