ResNet-34/DR: A Residual Convolutional Neural Network for the Diagnosis of Diabetic Retinopathy

Abstract

Diabetic retinopathy (DR) is an eye complication associated with diabetes, resulting in blurred vision or blindness. The early diagnosis and treatment of DR can decrease the risk of vision loss dramatically. However, such diagnosis is a tedious and complicated task due to the variability of retinal changes across the stages of the diseases, and due to the high number of undiagnosed and untreated DR cases. In this paper, we develop a computationally efficient and scalable deep learning model using convolutional neural networks (CNN), for diagnosing DR automatically. Various preprocessing algorithms are utilized to improve accuracy, and a transfer learning strategy is adopted to speed up the process. Our experiment used the fundus image set available on online Kaggle datasets. As an ultimate conclusion of applicable performance metrics, our computational simulation achieved a relatively-high F1 score of 93.2% for stage-based DR classification.