Deep Residual Network for Diagnosis of Retinal Diseases Using Optical Coherence Tomography Images

Diabetic retinopathy occurs due to damage to the blood vessels in the retina, and it is a major health problem in recent years that progresses slowly without recognizable symptoms. Optical coherence tomography (OCT) is a popular and widely used noninvasive imaging modality for the diagnosis of diabetic retinopathy. Accurate and early diagnosis of this disease using OCT images is crucial for the prevention of blindness. In recent years, several deep learning methods have been very successful in automating the process of detecting retinal diseases from OCT images. However, most methods face reliability and interpretability issues. In this study, we propose a deep residual network for the classification of four classes of retinal diseases, namely diabetic macular edema (DME), choroidal neovascularization (CNV), DRUSEN and NORMAL in OCT images. The proposed model is based on the popular architecture called ResNet50, which eliminates the vanishing gradient problem and is pre-trained on large dataset such as ImageNet and trained end-to-end on the publicly available OCT image dataset. We removed the fully connected layer of ResNet50 and placed our new fully connected block on top to improve the classification accuracy and avoid overfitting in the proposed model. The proposed model was trained and evaluated using different performance metrics, including receiver operating characteristic (ROC) curve on a dataset of 84,452 OCT images with expert disease grading as DRUSEN, CNV, DME and NORMAL. The proposed model provides an improved overall classification accuracy of 99.48% with only 5 misclassifications out of 968 test samples and outperforms existing methods on the same dataset. The results show that the proposed model is well suited for the diagnosis of retinal diseases in ophthalmology clinics. Graphical abstract