Deep Learning based Segmentation of Optical Coherence Tomographic Images of Human Saphenous Varicose Vein

Deep Learning based Segmentation of Optical Coherence Tomographic Images of Human Saphenous Varicose Vein

Deep-learning based segmentation model is proposed for Optical Coherence Tomography images of human varicose vein based on the U-Net model employing atrous convolution with residual blocks, which gives an accuracy of 0.9932.

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:arXiv.org 2023-03
Hauptverfasser: Viqar, Maryam, Madjarova, Violeta, Yadav, Amit Kumar, Pashkuleva, Desislava, Machikhin, Alexander S
Format: Artikel
Sprache:eng
Schlagworte:
Deep learning
Image segmentation
Optical Coherence Tomography
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Deep-learning based segmentation model is proposed for Optical Coherence Tomography images of human varicose vein based on the U-Net model employing atrous convolution with residual blocks, which gives an accuracy of 0.9932.
ISSN:2331-8422