Title: Classification of retinopathy of prematurity using back propagation neural network

Authors: Priya Rani; E.R. Rajkumar

Addresses: Department of Sensors and Biomedical Technology, School of Electronics Engineering, VIT University, Vellore, Tamil Nadu 632014, India ' Department of Sensors and Biomedical Technology, School of Electronics Engineering, VIT University, Vellore, Tamil Nadu 632014, India

Abstract: Retinopathy of Prematurity (ROP) is an ocular disease in premature infants and leads to blindness at its threshold stages. Thus, it should be diagnosed and treated at the right time to save the infants from permanent visual impairment. The aim of this work is to develop an efficient ROP stage detection tool. The work involves collection of ROP images, segmentation of features, as texture features, colour features and shape features, and finally feeding the features into the classifier to perform classification of the different stages. In this work, the classifier used is Back Propagation Neural Network (BPNN), and classification has been done into stages 3, 4 and 5 which mark the severity of the disease and call for immediate treatment. The results thus obtained are promising; hence, this work forms the basis for development of a semi-automated tool for the diagnosis of ROP.

Keywords: retinopathy of prematurity; ROP diagnosis; RetCam; image segmentation; feature extraction; back propagation neural networks; BPNN; classification; ocular disease; premature infants; premature babies; blindness; eye disease; visual impairment; blood vessels; texture features; colour features; shape features.

DOI: 10.1504/IJBET.2016.081221

International Journal of Biomedical Engineering and Technology, 2016 Vol.22 No.4, pp.338 - 348

Received: 07 Nov 2015
Accepted: 08 Mar 2016

Published online: 29 Dec 2016 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article