IJERT-EMS
IJERT-EMS

Classifying Diabetic Retinopathy using Deep Learning Architecture


Classifying Diabetic Retinopathy using Deep Learning Architecture
Authors : Chandrakumar T, R Kathirvel
Publication Date: 31-05-2016

Authors

Author(s):  Chandrakumar T, R Kathirvel

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Volume. 5 - Issue. 06 , June - 2016

e-ISSN:   2278-0181

 DOI:  http://dx.doi.org/10.17577/IJERTV5IS060055

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.

Citations

Number of Citations for this article:  Data not Available

Keywords

Key Word(s):    

Downloads

Number of Downloads:     1204
Similar-Paper

Call for Papers - May - 2017

        

 

                 Call for Thesis - 2017 

     Publish your Ph.D/Master's Thesis Online

              Publish Ph.D Master Thesis Online as Book