Direct Cup-To-Disc Ratio Estimation for Glaucoma Screening Via Semi-Supervised Learning

Abstract

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The Cup-to-Disc Ratio (CDR) serves as the most important indicator for glaucoma screening and plays a significant role in clinical screening and early diagnosis of glaucoma. In general, obtaining CDR is subjected to measuring on manually or automatically segmented optic disc and cup. Despite great efforts have been devoted, obtaining CDR values automatically with high accuracy and robustness is still a great challenge due to the heavy overlap between optic cup and neuroretinal rim regions. Automation of CDR estimation using Ml is proposed using three networks i.e. CNN, MLP, ResNet. Their performance in terms of accuracy and loss is compared and graphs are obtained and analyzed. The CNN model is often used for computer vision applications hence, it is compared with MLP and ResNet models to understand and compare performance.