Advancements in Glaucoma Diagnosis: A Comprehensive Literature Survey on CNN-based Deep Learning Approaches

Abstract

This literature survey review explores advancements in glaucoma diagnosis using convolutional neural networks (CNNs) within the realm of deep learning (DL). Glaucoma, a chronic and irreversible eye disease leading to vision deterioration, poses a significant global health challenge. Traditional diagnosis through colour fundus images is time-consuming, requiring skilled clinicians. The paper examines the development of a six-layered CNN architecture, integrating dropout and data augmentation techniques to enhance diagnostic accuracy. Focused on identifying intricate features like microaneurysms, exudate, and hemorrhages on the retina, this CNN-based approach offers a streamlined and efficient alternative to manual diagnosis. With glaucoma being a leading cause of blindness worldwide, the proposed methodology, trained on high-performance GPUs, presents a promising avenue for improving diagnostic efficiency and accuracy, thereby contributing to the evolution of glaucoma diagnosis