{"title":"BranchFusionNet: An energy-efficient lightweight framework for superior retinal vessel segmentation","authors":"Jing Qin, Zhiguang Qin, Peng Xiao","doi":"10.1007/s12083-024-01738-3","DOIUrl":null,"url":null,"abstract":"<p>In the rapidly advancing field of medical image analysis, accurate and efficient segmentation of retinal vessels is paramount for diagnosing ocular diseases, especially diabetic retinopathy. With the increasing emphasis on environmental sustainability, this paper presents BranchFusionNet, a novel lightweight neural network architecture tailored for retinal vessel segmentation. Embodying the principles of energy conservation, BranchFusionNet integrates multi-branch and lightweight dual-branch modules to optimize computational demands without sacrificing segmentation precision. This study not only contributes to the domain of retinal vessel segmentation but also showcases the potential of crafting energy-conscious deep learning methodologies in medical imaging applications.</p>","PeriodicalId":49313,"journal":{"name":"Peer-To-Peer Networking and Applications","volume":"126 1","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Peer-To-Peer Networking and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12083-024-01738-3","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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Abstract
In the rapidly advancing field of medical image analysis, accurate and efficient segmentation of retinal vessels is paramount for diagnosing ocular diseases, especially diabetic retinopathy. With the increasing emphasis on environmental sustainability, this paper presents BranchFusionNet, a novel lightweight neural network architecture tailored for retinal vessel segmentation. Embodying the principles of energy conservation, BranchFusionNet integrates multi-branch and lightweight dual-branch modules to optimize computational demands without sacrificing segmentation precision. This study not only contributes to the domain of retinal vessel segmentation but also showcases the potential of crafting energy-conscious deep learning methodologies in medical imaging applications.
期刊介绍：
The aim of the Peer-to-Peer Networking and Applications journal is to disseminate state-of-the-art research and development results in this rapidly growing research area, to facilitate the deployment of P2P networking and applications, and to bring together the academic and industry communities, with the goal of fostering interaction to promote further research interests and activities, thus enabling new P2P applications and services. The journal not only addresses research topics related to networking and communications theory, but also considers the standardization, economic, and engineering aspects of P2P technologies, and their impacts on software engineering, computer engineering, networked communication, and security.
The journal serves as a forum for tackling the technical problems arising from both file sharing and media streaming applications. It also includes state-of-the-art technologies in the P2P security domain.
Peer-to-Peer Networking and Applications publishes regular papers, tutorials and review papers, case studies, and correspondence from the research, development, and standardization communities. Papers addressing system, application, and service issues are encouraged.
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