Detection of Glaucoma Using Optic Disk Segmentation Based on CNN and VAE Models

Abstract

The leading eye disease commonly humans suffering is Glaucoma, which cause vision loss by progressively affecting peripheral vision. Presently, glaucoma diagnosis is performed by ophthalmologists, doctors, human professionals with the help of different medical equipment to analyze and understand for identifying the problematic eye images. Detecting glaucoma is a difficult task from the eye images with the help of existing data mining techniques. In this paper, we present an approach for diagnosing of glaucoma with the help of deep learning based techniques fundus images that are analyzed by making use of state-of-the-art deep learning techniques. Specifically, our approach towards glaucoma diagnosis using Convolutional Neural Networks (CNNs) and Varational auto encoder are used. The performance results show the comparative performance of proposed mechanism with state of art mechanisms.