Performance Analysis of Glaucoma Detection Techniques

Glaucoma is an incurable eye disorder and it is the second foremost reason for the visual deficiency. Commonly it is detected by the use of retina images. It is hard to anticipate glaucoma in beginning phases because the side effects of glaucoma can be identified only, once the sickness reaches at a high-level stage. Therefore, normal eye screening is fundamental and suggested. The manual method of glaucoma screening is a time-consuming and labor-intensive process. But deep learning-based glaucoma detection approaches diminish the manual effort and improve accuracy and speed. Optic cup to disc ratio is a major clinical pointer for glaucoma finding. For this ratio we need to perform segmentation of optic disc and cup and this can be performed via the segmentation methodologies based on deep learning. This paper presents a survey of different deep learning-based methodologies used for glaucoma detection.