Prediction of Pigment Epithelial Detachment in Optical Coherence Tomography Images using Machine Learning

Pigment Epithelial Detachment (PED) is an eye condition that can affect adults over 50 and eventually harm their central vision. The PED region is positioned between the Bruch's membrane (BM) and the RPE (Retinal Pigment Epithelium) layer. Due to PED, the RPE layer is elevated arc shaped. In this paper, a method to extract the best features to detect pigment epithelial detachment (PED) is proposed. This method uses four-stage strategy that drew inspiration from OCT (Optical Coherence Tomography) imaging to detect the PED. In the first stage, to reduce the speckle-noise, in the second stage, segment the Retinal Pigment Epithelium (RPE) layer. In the third stage, a novel method is proposed to extract the best features to detect PED, and in the fourth stage, machine learning classifiers such as K-Nearest Neighbors (KNN), Logistic Regression (LR), Naive ayes (NB), and Artificial Neural Networks (ANN) were used to significantly predict the PED. For experimental results, 150 retinal OCT volumes were used, 75 normal OCT volumes, and 75 pigment epithelial detachment volumes. Among the 150 images, 80% were used for training and 20% were used for testing. Here, there are 30 images for testing and 120 images for training. To generate a confusion matrix based on the matrices are true positive (TP), false positive (FP), true negative (TN), and false negative (FN). Logistic Regression is predicted more accuracy among the ANN, LR, NB, and KNN models. The LR model predicted accuracy 96.67% for PED detection.