Classifying Diabetic Retinopathy using Deep Learning Architecture

Abstract

A recent development in the state-of-art technology machine learning plays a vital role in the image processing applications such as biomedical, satellite image processing, Artificial Intelligence such as object identification and recognition and so on. In Global, diabetic retinopathy suffered patients growing vastly. And the fact is earliest stage could not diagnoses to normal eye vision. Increasing necessity of finding a diabetic retinopathy as earliest would stops vision loss for prolonged diabetes patient although suffered youngs’. Severity of the diabetic retinopathy disease is based on a presence of microaneurysms, exudates, neovascularization, Haemorrhages. Experts are categorized those diabetic retinopathy in to five stages such as normal, mild, moderate, severe Non- proliferative(NPDR) or Proliferative diabetic retinopathy patient(PDR). A proposed deep learning approach such as Deep Convolutional Neural Network(DCNN) gives high accuracy in classification of these diseases through spatial analysis. A DCNN is more complex architecture inferred more from human visual perspects. Amongst other supervised algorithms involved, proposed solution is to find a better and optimized way to classifying the fundus image with little pre-processing techniques. Our proposed architecture deployed with dropout layer techniques yields around 94-96 percent accuracy. Also, it tested with popular databases such as STARE, DRIVE, kaggle fundus images datasets are available publicly. Keywords