Diabetic Retinopathy Detection Using 3D OCT Features

If untreated, diabetic retinopathy (DR) can result in a severe health complication, leading to visual loss. This study focuses on developing a computer-assisted diagnostic (CAD) system that utilizes 3D optical coherence tomography (OCT) images for detecting DR. To begin with, the 3D OCT images are subjected to a process where the retinal layers are isolated from the input. Following this, from each individual retinal layer, two key 3D characteristics, namely thickness and first-order reflectivity, are computed. Eventually, classification is carried out using backpropagation neural networks. Utilizing 10-folds cross-validation on 188 cases, experiments validate the benefits of the developed system over competing approaches, with an accuracy of 94.74% ± 5.55%. These results demonstrate the method’s potential for DR detection utilizing OCT images.