Multi-Branch Convolutional Neural Network Architecture for Glaucoma Diagnosis Using Optical Coherence Tomography Biomarkers and Synthetic Image Simulation

Multi-Branch Convolutional Neural Network Architecture for Glaucoma Diagnosis Using Optical Coherence Tomography Biomarkers and Synthetic Image Simulation

This paper presents a multi-branch convolutional neural network designed for glaucoma diagnosis using optical coherence tomography biomarkers and synthetic image simulations. The network includes six branches, each targeting key anatomical features. Trained on a synthetic dataset, the model achieved...

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Bibliographic Details
Main Authors: Ph. V. Usenko, A. M. Prudnik
Format: Article
Language:Russian
Published: Educational institution «Belarusian State University of Informatics and Radioelectronics» 2025-02-01
Series:Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki
Subjects:
glaucoma diagnosis
optical coherence tomography
convolutional neural network
optical coherence tomography biomarkers
synthetic data generation
image segmentation
deep learning in ophthalmology
Online Access:https://doklady.bsuir.by/jour/article/view/4066
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