  Retinopathy of Prematurity (ROP) is a fibrovascular proliferative disorder,
which affects the developing peripheral retinal vasculature of premature
infants. Early detection of ROP is possible in stage 1 and stage 2
characterized by demarcation line and ridge with width, which separates
vascularised retina and the peripheral retina. To detect demarcation line/
ridge from neonatal retinal images is a complex task because of low contrast
images. In this paper we focus on detection of ridge, the important landmark in
ROP diagnosis, using Convolutional Neural Network(CNN). Our contribution is to
use a CNN-based model Mask R-CNN for demarcation line/ridge detection allowing
clinicians to detect ROP stage 2 better. The proposed system applies a
pre-processing step of image enhancement to overcome poor image quality. In
this study we use labelled neonatal images and we explore the use of CNN to
localize ridge in these images. We used a dataset of 220 images of 45 babies
from the KIDROP project. The system was trained on 175 retinal images with
ground truth segmentation of ridge region. The system was tested on 45 images
and reached detection accuracy of 0.88, showing that deep learning detection
with pre-processing by image normalization allows robust detection of ROP in
early stages.
