Retinal Biomarkers for Detecting Diabetic Retinopaty Using Smartphone-Based Deep Learning Frameworks

Abstract

Convolutional neural networks (CNNs) have shown success in detecting Diabetic Retinopathy (DR) from retinal images captured by high-quality fundus cameras. As an alternate solution, smartphone-based systems with limited field-of-view (FoV) are recently proposed for DR screening. This paper investigates where to focus on the retina when using such smartphone-based devices. After training a CNN on original fundus images from diverse datasets, we evaluate the trained model on various regions of the retina (the fovea, the optic disc, the center of fovea and optic disc, the center of lower fovea and optic disc, and center of upper fovea and optic disc) that could be most effective to determine DR. Our experiments show that retinal images from smartphone-based systems with a narrower FoV (40%) that covers around fovea provided very close performance to original images with 0.963 AUC (within 0.99 of the optimum).