Deep Learning Assisted Retinopathy of Prematurity Screening Technique

Retinopathy of Prematurity (ROP) is the leading cause of blindness in preterm babies worldwide. By using proper scanning and treatment, the effect of the blindness of ROP can be reduced. However, due to lack of medical facilities, a large proportion of these preterm infants remain undiagnosed after birth. As a result, these babies are more likely to have ROP induced blindness. In this paper, we propose a robust and intelligent system based on deep learning and computer vision to automatically detect the optical disk (OD) and retinal blood vessels and also classify the high severity (Zone-1) case of ROP. To test and validate the proposed system, we present empirical results using the preterm infant fundus images from a local hospital. Our results showed that the YOLO-V5 model accurately detects the OD from preterm babies fundus images. Further, the computer vision-based system accurately segmented the retinal vessels from the preterm babies fundus images. Specifically for the Zone-1 case of ROP, our system is able to achieve an accuracy of 83.3%.