Affiliations: [a] Department of Electronics and Communication, College of Engineering Trivandrum, Kerala, India
| [b] Department of Pediatric and Tele-ROP Services, Narayana Nethralaya Eye Hospital, Bangalore, India
| [c] Department of Biomedical Engineering, Indian Institute of Technology Hyderabad, Telangana, India
Correspondence:
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Corresponding author. Renu John, Department of Biomedical Engineering, Indian Institute of Technology Hyderabad, Telangana, 500007, India. E-mail: [email protected].
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.
