Accurate detection of non-proliferative diabetic retinopathy in optical coherence tomography images using convolutional neural networks
Diabetic retinopathy (DR) is a disease that forms as a complication of diabetes, It is particularly dangerous since it often goes unnoticed and can lead to blindness if not detected early. Despite the clear importance and urgency of such an illness, there is no precise system for the early detection of DR so far. Fortunately, such system could be achieved using deep learning including convolutional neural networks (CNNs), which gained momentum in the field of medical imaging due to its capability of being effectively integrated into various systems in a manner that significantly improves the performance. This paper proposes a computer aided diagnostic (CAD) system for the early detection of non-proliferative DR (NPDR) using CNNs. The proposed system is developed for the optical coherence tomography (OCT) imaging modality. Throughout this paper, all aspects of deployment of the proposed system are studied starting from the preprocessing stage required to extract input data to train the CNN without resizing the image, to the use of transfer learning principals and how best to combine features in order to optimize performance. A novel patch extraction framework for preprocessing is presented, followed by fovea detection algorithm, in addition to investigating the various CNN parameters for optimal deployment. Optimum CNN parameters and promising results are achieved. To the best of our knowledge, this is the first CNN-based DR early detection CAD system for OCT images. It achieves a promising accuracy of 94% with transfer learning.