Deep Neural Networks Model to Detection Glaucoma in Prima Phase

Abstract

The glaucoma is an eye disease that can lead to blindness if it is not detected and treated on time. It is often associated with the increase in the intraocular pressure (IOP) of the fluid (known as aqueous humor) in the eye, and it has been nick-named the ‘Silent Thief of Sight’. In glaucoma, the optic nerve can be affected directly, and in such a case, it may be led to permanent or progressive loss of vision. Clinical treatment point of view and detection of the glaucoma at an early stage can be very helpful. The manual study of the ophthalmic images is a time-consuming process. Unlike existing methods, CNN will automatically extract the features from raw images, that can finally use to train the classifier, in which the classifier can classify the images into their respective abnormalities. In this article, deep convolutional neural network is suggested, which can recognize the multi-faceted features through apply image processing techniques on the digital fundus images of the eye for the analysis of glaucoma and normal eye.