Open AccessAutomated Detection and Classification of Diabetic Retinopathy using ConvNets
Author(s) -
B Aavani
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.38628
Subject(s) - diabetic retinopathy , fundus (uterus) , retinopathy , blindness , convolutional neural network , medicine , artificial intelligence , confusion matrix , ophthalmology , confusion , optometry , computer science , diabetes mellitus , psychology , psychoanalysis , endocrinology
Diabetic retinopathy is the leading cause of blindness in diabetic patients. Screening of diabetic retinopathy using fundus image is the most effective way. As the time increases this DR leads to permanent loss of vision. At present, Diabetic retinopathy is still being treated by hand by an ophthalmologist which is a time-consuming process. Computer aided and fully automatic diagnosis of DR plays an important role in now a day. Data-set containing a collection of fundus images of different severity scale is used to analyze the fundus image of DR patients. Here the deep neural network model is trained by using this fundus image and five-degree classification task is performed. We were able to produce an sensitivity of 90%. Keywords: Confusion matrix, Deep convolutional Neural Network, Diabetic Retinopathy, Fundus image, OCT