Automated Staging and Grading for Retinopathy of Prematurity on Indian Database

Abstract

Retinopathy of prematurity (ROP) is a disorder of the retina in neonates. If ROP is not treated at early stage, neonates’ vision is affected, leading to blindness. It is necessary to diagnose and treat ROP at earliest. Several ROP assessment techniques based on Image analysis have been introduced in recent years. These studies identify only normal, abnormal, and plus disease. This research article explores the identification of distinct ROP stages along with normal and abnormal detection. Detecting the stages will help to expedite the treatment and prevent vision loss. The proposed framework consists of feature extraction using scale-invariant feature transform (SIFT) and pyramid histogram of words (PHOW) techniques. Three efficient supervised machine learning algorithms, namely random forest (RF), support vector machine (SVM), and extreme boosting gradient (XGBoost), are used to classify different stages of ROP. A dataset captured by RetCam 3 is used to evaluate the model. Based on rigorous evaluation, the accuracy of different ROP stages is 93.68%, 83.33%, 85.71%, 55.55%, and 100% for normal, stage 1, stage 2, stage 3, and stage 4, respectively.