Comparative analysis of deep learning methods of detection of diabetic retinopathy

Abstract

Diabetic retinopathy is a common complication of diabetes, that affects blood vessels in the light-sensitive tissue called the retina. It is the most common cause of vision loss among people with diabetes and the leading cause of vision impairment and blindness among working-age adults. Recent progress in the use of automated systems for diabetic retinopathy diagnostics has offered new challenges for the industry, namely the search for a less resource-intensive architecture, e.g., for the development of low-cost embedded software. This paper proposes a comparison between two widely used conventional architectures (DenseNet, ResNet) with the new optimized one (EfficientNet). The proposed methods classify the retinal image as one of 5 class cases based on the dataset obtained from the 4th Asia Pacific Tele-Ophthalmology Society (APTOS) Symposium.