Automatic detection and classification of diabetic retinopathy stages using CNN

Document Type

Conference Article

Publication Title

2017 4th International Conference on Signal Processing and Integrated Networks, SPIN 2017

Abstract

A Convolutional Neural Networks (CNNs) approach is proposed to automate the method of Diabetic Retinopathy(DR) screening using color fundus retinal photography as input. Our network uses CNN along with denoising to identify features like micro-Aneurysms and haemorrhages on the retina. Our models were developed leveraging Theano, an open source numerical computation library for Python. We trained this network using a high-end GPU on the publicly available Kaggle dataset. On the data set of over 30,000 images our proposed model achieves around 95% accuracy for the two class classification and around 85% accuracy for the five class classification on around 3,000 validation images.

First Page

550

Last Page

554

DOI

10.1109/SPIN.2017.8050011

Publication Date

9-25-2017

This document is currently not available here.

Share

COinS