Enhanced Detection of Referable Diabetic Retinopathy via DCNNs and Transfer Learning

Abstract

A clinically acceptable deep learning system (DLS) has been developed for the detection of diabetic retinopathy by the Singapore Eye Research Institute. For its utility in a national screening programme, further enhancement was needed. With newer deep convolutional neural networks (DCNNs) being introduced and technological methodology such as transfer learning gaining recognition for better performance, this paper compared the performance of the DCNN used in the original DLS, VGGNet, with newer DCNNs, ResNet and Ensemble, with transfer learning. The DLS performance improved with higher AUC, sensitivity and specificity with the adoption of the newer DCNNs and transfer learning.