An Analysis Of Segmentation And Glaucoma Diagnosis By Deep Learning Technique Via Retinal Fundus Images

Abstract

A leading retinal disease was Glaucoma. It damages the eye due to the intraocular pressure on the eye. When glaucoma was untreated left, it would led to the loss of vison by affecting the ONH. (optic nerve head) The glaucoma progression was investigated on the retina of eye through ophthalmologist. This method was very boring and it enhance more time to do by human beings. Therefore, this tiuuse was the right problem which may be solved by diagnosing automatically with the support of the approach of deep learning process. CNNs are suitable to identify the solution for this problem. It may extract different stages of information from the input picture and motivates to variate among glaucomica and non-glaucomic pictures. This examined paper presents an effective glaucoma structure to fragment the optic disc and cup to identify the CDR value. The glaucoma was achieved by deep learing process along with the novel Convolutional Neural Network. An examined method uses 2 similar Convolutional neural network architecture to extract the optic disc and otpic cup to get perfect output. This deisgn was tested and trained on DRISHRI –GS data base, that was available publicly and an exact value of 97% for OC and 98% for OD segmentation was investigated.