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Peer-to-Peer Networking and Applications
Erschienen in:

20.06.2024

BranchFusionNet: An energy-efficient lightweight framework for superior retinal vessel segmentation

verfasst von: Jing Qin, Zhiguang Qin, Peng Xiao

Erschienen in: Peer-to-Peer Networking and Applications | Ausgabe 5/2024

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Abstract

Der Artikel stellt BranchFusionNet vor, ein neuartiges neuronales Netzwerk zur energieeffizienten und leichten Segmentierung retinaler Gefäße. Dieses Rahmenwerk befasst sich mit den Herausforderungen der Erkennung komplizierter Netzhautgefäße, die für die Diagnose von Erkrankungen wie der diabetischen Retinopathie von entscheidender Bedeutung sind. BranchFusionNet reduziert die Rechenkomplexität, indem es branchenübergreifende Module und leichtgewichtige zweibranchige Designs integriert und gleichzeitig eine hohe Genauigkeit beibehält. Die Methode wurde anhand öffentlicher Datensätze wie DRIVE, STARE und CHASE _ DB1 validiert und zeigte im Vergleich zu bestehenden Methoden eine überlegene Leistung bei Genauigkeit, Sensitivität und F1-Werten. Die innovative Architektur von BranchFusionNet bietet eine vielversprechende Lösung für eine nachhaltige und effektive medizinische Bildgebung, insbesondere in Umgebungen mit begrenzten Ressourcen im Gesundheitswesen.
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Metadaten
Titel
BranchFusionNet: An energy-efficient lightweight framework for superior retinal vessel segmentation
verfasst von
Jing Qin
Zhiguang Qin
Peng Xiao
Publikationsdatum
20.06.2024
Verlag
Springer US
Erschienen in
Peer-to-Peer Networking and Applications / Ausgabe 5/2024
Print ISSN: 1936-6442
Elektronische ISSN: 1936-6450
DOI
https://doi.org/10.1007/s12083-024-01738-3

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