Automated Glaucoma Diagnosis Using Vision Transformer and Deep Learning Techniques on Retinal Fundus Images

Abstract

Glaucoma is one of the leading causes of irreversible blindness worldwide. Early diagnosis is critical to preventing permanent optic nerve damage. Traditional glaucoma diagnosis relies heavily on ophthalmologists’ expertise and manual examination of retinal fundus images, which can be time-consuming and subjective. This paper proposes a Novel Glaucoma Detection framework using Advanced Deep Learning and Vision Transformer (ViT)-based architectures for automated analysis of ophthalmic fundus images. The proposed system integrates image preprocessing, optic disc localization, data augmentation, and a hybrid Swin Transformer–CNN classification model for accurate glaucoma screening. Publicly available datasets such as REFUGE, ORIGA, and Drishti-GS are utilized for evaluation. Experimental results demonstrate superior performance compared with conventional CNN models. The proposed framework shows strong generalization capability and can assist ophthalmologists in early glaucoma diagnosis and large-scale screening programs.