Automatic Detection of Eye Cataract using Deep Convolution Neural Networks (DCNNs)

Eye cataract is a condition in which the lens of the eye becomes clouding or less transparent. This affects the clear vision and is the most prevailing causes of blindness. Therefore, early cataract detection and prevention may reduce the blindness rate and surgery pain of the patients. This paper presents an eye cataract detection system using Deep Convolution Neural Network (DCNNs) comprising two modules: training and testing. The proposed DCNNs architecture is trained, validated and tested with retinal fundus images. Experimental result shows that the proposed system is capable of detecting eye cataract with high accuracy.

I. Introduction

Eyes are the unique vision gland in the human body and many people are suffering from eye disorder causing vision impairments. Cataract is one of the most prevalent eye diseases which is the frequent reason for blindness. Fig. 1 shows a retinal fundus image which represents the normal retinal fundus image that visible the capillaries and vascular cell (Fig. 1a). Fig. 1b shows a cataract image in which capillaries and vascular are not visible due to blurriness. At this stage, vision is lost in most of the people.