Diabetic Retinopathy Detection with Deep Learning Techniques on Fundus Photographs: A Review

Abstract

Diabetic retinopathy (DR) is an ocular condition that can affect individuals with diabetes mellitus (DM) and may lead to reduced vision or even blindness if not detected on time. Delay in diagnosis and disagreement in interpretation of retinal images by different health experts are some of the challenges that can occur during screening for DR. Deep learning (DL) techniques are currently used for classification of images across various domains including the ophthalmic imaging field. The implementation of this cutting edge technology for detection of DR could lead to improvement of existing eye care services for diabetic individuals. This paper discussed the publicly available datasets of retinal images of diabetic individuals used for training DL models. The efficiency of several convolutional neural networks (CNNs) created for the detection of different classes of DR was also reviewed. Furthermore, the achievements and the challenges faced in the application of DL techniques for the DR detection were discussed. Finally, future works that can be performed in this research area has been suggested.