Multi-View Machine Learning with an Optic Disc Localization for Glaucoma Diagnosis

Glaucoma affects a significant proportion of people worldwide, and if it progresses to a severe stage, it can lead to blindness. Furthermore, screening and accurately diagnosing glaucoma present a challenge for ophthalmologists. Early detection of glaucoma is crucial because it allows for timely treatment, potentially preventing severe complications that could lead to blindness. Typically, ophthalmologists diagnose glaucoma by analyzing eye fundus photographs to assess the ratio of the optic cup and optic disc (CDR). Machine learning algorithms can assist in glaucoma detection by classifying fundus images. This study introduces image preprocessing techniques for optic disc localization, combined with an integrating multi-view network for accurate glaucoma classification. The dataset used in this research was obtained from Naresuan University Hospital. The study found that EfficientNet underwent training using the Adam optimizer at a fixed learning rate of 0.0001. The multi-view network achieved Accuracy 90.48%, AUC 95.14%, Precision 81.95%, Recall 75.90%, and F1-score 78.72%. This study presents an effective approach to assist ophthalmologists in detecting early-stage glaucoma and glaucoma, thereby improving diagnostic efficiency.