Assessment of transfer learning models for grading of diabetic retinopathy

Abstract

Diabetic retinopathy is a potentially mortal diabetic complication. The severity level of DR must be identified earlier to reduce the medical complications. Effective automated ways for identifying DR and classifying its severity stage are necessary to reduce the burden on ophthalmologists. Transfer learning methods are utilized to automatically grade the  severity of diabetic retinopathy in this study. The stages of DR are diagnosed using pretrained VGG16, Inception v3, and ResNet50 models on pre-processed retinal images of DDR dataset. Out of three implemented models, Inception v3 achieved higher validation accuracy of 76.47% and testing accuracy of 67% compared to VGG16 and ResNet50 models. This research contributes to the analysis of deep learning architectures for the creation of automated diabetic retinopathy stage diagnosis and grading