Open AccessDetection of Diabetic Retinopathy using Convolutional Neural Network
Author(s) -
Razat Agarwal*,
Aditya Mahamuni,
Noopur Gautam,
Piyush Awachar,
Prof. Parth Sagar
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c6303.118419
Subject(s) - computer science , diabetic retinopathy , convolutional neural network , fundus (uterus) , artificial intelligence , segmentation , task (project management) , pattern recognition (psychology) , retinopathy , computer vision , diabetes mellitus , medicine , ophthalmology , management , economics , endocrinology
Diabetic Retinopathy is a medical condition in which damage occurs to the retina due to diabetes mellitus. The diagnosis of Diabetic Retinopathy through colored fundus images stand in need of experienced clinicians to identify the presence and significance of many small features, which makes it a time consuming task. In this paper, we propose a CNN based approach to detect Diabetic Retinopathy in fundus images. Data used to train the model is prepocessed by a new segmentation technique using Gabor filters. Due to small dataset, data augmentation is done to get enough data to train the model. Our segmentation model detects intricate features in the fundus images and detect the presence of DR. A high-end Graphics Processor Unit (GPU) is used to train the model efficiently. The publicly available Kaggle Dataset is used to demonstrate impressive results, particularly for a high-level classification task. On the training dataset of 14,650 images, our proposed CNN achieves a specificity of 94% and an accuracy of 69% on 3,660 validation images.