Automated Recognition of Retinopathy of Prematurity with Deep Neural Networks

Abstract

Retinopathy of Prematurity (ROP) is a blinding disease, which primarily occurs on premature infants whose birth weights is less than 1250 grams or gestation is less than 31 weeks. ROP has become the leading cause of preventable childhood blindness throughout the world. Nowadays, more and more researchers start attempting to develop auto or semi-auto methods based on digital image analysis to diagnose ROP. However, factors like high measurement errors or redundant analysis phrases make traditional analysis methods difficult to assist diagnose ROP perfectly. In this paper, we develop an automated system to analyse premature infants' retinal images using deep neural networks. We primarily try to solve two problems. (1) the existence of ROP, normal or ROP; (2) the severity of ROP, mild-ROP or severe-ROP. Deep neural networks take the advantages of strong representation ability and enable to nonlinear mapping, attain high accuracies and great generalization performances on retinal fundus image datasets.