Multi-Branch Convolutional Neural Network Architecture for Glaucoma Diagnosis Using Optical Coherence Tomography Biomarkers and Synthetic Image Simulation
https://doi.org/10.35596/1729-7648-2025-23-1-74-82
Abstract
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 a validation accuracy of 94.2 % and a training loss of 0.162, demonstrating effectiveness in distinguishing between different glaucoma types. The results also highlight the potential for further accuracy improvement, particularly in reducing classification errors between closely related conditions.
Keywords
glaucoma diagnosis,
optical coherence tomography,
convolutional neural network,
optical coherence tomography biomarkers,
synthetic data generation,
image segmentation,
deep learning in ophthalmology
Supporting agencies: We express our sincere gratitude to the staff of the Department of Ophthalmology of the Institute for Advanced Training and Retraining of Healthcare Personnel of the Belarusian State Medical University, candidate of medical sciences, Associate Professor Oksana Nikolaevna Dudich and Doctor of Medical Sciences, Professor Victoria Leonidovna Krasilnikova for valuable discussions and constructive comments that contributed to the improvement of this article.
About the Authors
Ph. V. Usenko
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Postgraduate at the Engineering Psychology and Ergonomics Department
Minsk
A. M. Prudnik
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Prudnik Aleksander Mikhailovich, Cand. of Sci., Associate Professor, Associate Professor at the Engineering Psychology and Ergonomics Department
220013, Minsk, P. Brovki St., 6
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Review
For citations:
Usenko P.V., Prudnik A.M. Multi-Branch Convolutional Neural Network Architecture for Glaucoma Diagnosis Using Optical Coherence Tomography Biomarkers and Synthetic Image Simulation. Doklady BGUIR. 2025;23(1):74-82. https://doi.org/10.35596/1729-7648-2025-23-1-74-82
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