Early Signs Detection of Diabetic Retinopathy Using Optical Coherence Tomography Angiography Scans Based on 3D Multi-Path Convolutional Neural Network

Diabetic Retinopathy (DR) is one of the leading causes of blindness in working age population worldwide. DR is caused by high blood sugar levels (diabetes), which damages retinal blood vessels and leads to vision loss. The diagnosis of DR requires manual measurements and visual assessment of the changes that happen in the retina, which is highly complex task. Thus, there is an unmet clinical need for a non-invasive, and objective diagnostic system that can improve the accuracy of early diagnosis of DR. In this paper, we develop and validate a computer-aided diagnostic (CAD) system for highly accurate, early diagnosis of DR. The proposed system use a 3D convolutional neural network (CNN) to segment blood vessels from both superficial and deep plexuses of optical coherence tomography angiography (OCTA) scans. Four significant retinal vasculature features are extracted, which reflect the changes in the retinal blood vessels due to DR progress. Finally, these extracted features are classified by using the random forest (RF) technique to differentiate the early DR from normal subjects. Our proposed system achieved an average accuracy of 98%, sensitivity of 98%, and specificity of 100%, which outperforms other state-of-the-art techniques.