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

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.