Automated identification and grading system of diabetic retinopathy using deep neural networks

Abstract Diabetic retinopathy (DR) is a major cause of human vision loss worldwide. Slowing down the progress of the disease requires early screening. However, the clinical diagnosis of DR presents a considerable challenge in low-resource settings where few ophthalmologists are available to care for all patients with diabetes. In this study, an automated DR identification and grading system called DeepDR is proposed. DeepDR directly detects the presence and severity of DR from fundus images via transfer learning and ensemble learning. It comprises a set of state-of-the-art neural networks based on combinations of popular convolutional neural networks and customised standard deep neural networks. The DeepDR system is developed by constructing a high-quality dataset of DR medical images and then labelled by clinical ophthalmologists. We further explore the relationship between the number of ideal component classifiers and the number of class labels, as well as the effects of different combinations of component classifiers on the best integration performance to construct an optimal model. We evaluate the models on the basis of validity and reliability using nine metrics. Results show that the identification model performs best with a sensitivity of 97.5%, a specificity of 97.7% and an area under the curve of 97.7%. Meanwhile, the grading model achieves a sensitivity of 98.1% and a specificity of 98.9%. On the basis of the methods above, DeepDR can detect DR satisfactorily. Experiment results indicate the importance and effectiveness of the ideal number and combinations of component classifiers in relation to model performance. DeepDR provides reproducible and consistent detection results with high sensitivity and specificity instantaneously. Hence, this work provides ophthalmologists with insights into the diagnostic process.