Classification of OCT Images for Detecting Diabetic Retinopathy Disease using Machine Learning

Abstract

Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease because of their capability to capture micrometer-resolution. An automated technique was introduced to differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160 images were used for classifiers’ training, and 54 images were used for testing. Different features were extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features. The experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal retina with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 100%. The retinal OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP features has a significant impact on the achieved results. The result has better performance than previously proposed methods in the literature.