A fully convolutional neural network approach for the localization of optic disc in retinopathy of prematurity diagnosis

Abstract

The identification of landmark features such as optic disc is of high prognostic significance in diagnosing various ophthalmic diseases. A retinal fundus photograph provides a non-invasive observation of the optic disc. The wide variability present in fundus images poses difficulties in its detection and further analysis. The reported work is a part of the fundus image screening for the diagnosis of Retinopathy of Prematurity (ROP), a sight threatening disorder seen in preterm infants. The diagnostic procedure for this disease estimates blood vessel tortuosity in a pre-defined area around the optic disc. Hence accurate optic disc localization is very important for the disease diagnosis. In this paper, we present an optic disc localization technique using a deep neural network based framework. The proposed system relies on the underlying architecture of YOLOv3, a fully convolutional neural network pipeline for object detection and localization. The new approach is tested in 10 different data sets and has achieved an overall accuracy of 99.25%, outperforming other deep learning-based OD detection methods. The test results guarantees the robustness of the proposed technique, and hence may be deployed to assist medical experts for disease diagnosis.