Identifying Diabetic Retinopathy from OCT Images using Deep Transfer Learning with Artificial Neural Networks

Abstract

Diabetic retinopathy occurs when the blood vessels inside the retina are damaged as a result of diabetes. Early diagnosis and treatment of this disease is crucial to avoid blindness. Analysis of retinal images such as funduscopy, ultrasonography, and optical coherence tomography (OCT) is typically used in the diagnosis of diabetic retinopathy. In recent years, various automated techniques including deep learning have been used for this purpose. In this paper, we explore how to use deep transfer learning for the diagnosis of diabetic retinopathy using OCT images. We retrain existing deep learning models for this task and investigate how a retrained model can be optimized. We demonstrate that using an optimized pre-trained model as a feature extractor and training a conventional classifier on these features is an effective way to diagnose diabetic retinopathy using OCT images. We show through experiments that the proposed method outperforms similar existing methods with respect to accuracy and training time.