An Early Detection of Diabetic Retinopathy from Fundus Images using Deep Learning

Abstract

Diabetes is the most common disease that affects people who are suffering with high glucose levels in blood. It
has many complications such as Neuropathy, Nephropathy, Retinopathy, etc. Among all these complications
Diabetic Retinopathy (DR) is the most founded disease in Diabetic patients. DR occurs when the retinal blood
vessels in the eye is damaged. Now-a-days DR is the common disease by which blindness occurs globally. The
detection of DR manually is expensive, and time taken. As performing retinal screening examinations is a time
taken and unmet need on all diabetic patients, automated medical image analysis helps in identifying the disease
severity. In this study the image classification using deep learning is explained. The purpose of this work is to
develop an automated diagnostic system for Diabetic Retinopathy screening. Here using deep learning, pretrained
models (ResNet50, VGG-16 and VGG-19) are used for DR classification. Pre-trained models are very
helpful in detecting the DR easily. KAGGLE Asia Pacific Tele-Ophthalmology Society (APTOS) dataset which
consists of 3662 fundus images of five different classes namely Normal, Mild, Moderate, Severe and PDR
(Proliferative DR) is used as training data for pre-trained models. Due to class imbalance after applying preprocessing
on the images only 3500 images were fed to the network. The model is processed on 3500 fundus
images and the output will produce the severity of DR. VGG-19 gained highest accuracy of 89.00% than other
models. In this study KAGGLE dataset is taken to detect DR images, In future, we will try different datasets
implementing on this model to increase the accuracy in detecting DR.