A novel deep learning framework for the identification of tortuous vessels in plus diseased infant retinal images

Article type: Research Article

Authors: Ramachandran, Sivakumar

Affiliations: Department of Electronics and Communication, Government Engineering College Wayanad, Kerala, India | E-mail: sivan@gecwyd.ac.in

Correspondence: [*] Corresponding author: Department of Electronics and Communication, Government Engineering College Wayanad, Kerala, India. E-mail: sivan@gecwyd.ac.in.

Abstract: Retinopathy of prematurity (ROP), sometimes known as Terry syndrome, is an ophthalmic condition that affects premature babies. It is the main cause of childhood blindness and morbidity of vision throughout life. ROP frequently coexists with a disease stage known as Plus disease, which is marked by severe tortuosity and dilated retinal blood vessels. The goal of this research is to create a diagnostic technique that can discriminate between infants with Plus disease from healthy subjects. Blood vascular tortuosity is used as a prognostic indicator for the diagnosis. We examine the quantification of retinal blood vessel tortuosity and propose a computer-aided diagnosis system that can be used as a tool for ROP identification. Deep neural networks are used in the proposed approach to segment retinal blood vessels, which is followed by the prediction of tortuous vessel pixels in the segmented vessel map. Digital fundus images obtained from Retcam3TM is used for screening. We use a proprietary data set of 289 infant retinal images (89 with Plus disease and 200 healthy) from Narayana Nethralaya in Bangalore, India, to illustrate the efficacy of our methodology. The findings of this study demonstrate the reliability of the proposed method as a computer-aided diagnostic tool that can help medical professionals make an early diagnosis of ROP.

Keywords: Plus disease, vessel segmentation, convolutional neural network, tortuosity estimation, ROP diagnosis

DOI: 10.3233/IDA-220451

Journal: Intelligent Data Analysis, vol. 27, no. 5, pp. 1523-1537, 2023