AIoT and Big Data Analytics for Smart Healthcare Applications

Stage of Retinopathy of Prematurity Using CNN and Object Segmentation Technique

Author(s): Jothimani K. * .

Pp: 225-239 (15)

DOI: 10.2174/9789815196054123050016

* (Excluding Mailing and Handling)

Abstract

Premature adolescents with Retinopathy of Prematurity (ROP), a fibrovascular proliferative condition, have difficulties with the maturing peripheral retinal vasculature. Early identification of ROP is achievable in stages 1 and 2, distinguished by a demarcation line and ridge that divides the peripheral retina from the vascularized retina. Because newborn retinal images have poor contrast, it is difficult to distinguish demarcation lines or ridges. This study used segmentation and convolutional neural networks to detect ridges, which are crucial landmarks in the diagnosis of ROP. Our contribution is implementing Mask R-CNN for identifying boundary line/ridge recognition, which enables doctors to identify ROP stage 2 more accurately. To combat poor image quality, the suggested approach uses a pre-processing stage of image augmentation. In this study, the utility of the Convolutional Neural Network was examined to localize ridges in labeled neonatal photos. The KIDROP study and a dataset comprising 220 photos of 45 infants were used. Using the segmentation of the ridge region as the ground truth, 175 retinal images were used to train the system. The system's detection accuracy was 0.94, with 45 images under test, proving that data augmentation detection in conjunction with image normalizing pre-processing allows accurate identification of the ROP in its early stages.


Keywords: ROP, Convolutional Neural Network, R-Convolutional Neural Network, Enhancement.

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