A vision-GNN framework for retinopathy classification using optical coherence tomography

Abstract

Retinopathy refers to pathologies of the retina that can ultimately result in vision impairment and blindness. Optical Coherence Tomography (OCT) is a technique to image these diseases, aiding in the early detection of retinal damage, which may mitigate the risk of vision loss. In this work, we propose an end-to-end Graph Neural Network (GNN) pipeline that can extract deep graph-based features for multi-class retinopathy classification for the first time. To our knowledge, this is also the first work applying Vision-GNN for OCT image analysis. We trained and tested the proposed GNN on a public OCT retina dataset divided into four categories (Normal, Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen). Using our method, we achieve an average accuracy of 99.07% over four classes proving the effectiveness of a deep learning classifier for OCT images with graph-based features. This work lays the foundation to apply GNNs for OCT imaging to aid the early detection of retinal damage.

Mobile-friendly skin lesion detection using an attention-driven lightweight model

“…The rapid development of machine learning techniques, especially deep learning, has greatly improved the automated medical imaging systems, leading to unprecedented performance in the domain of lesion/organ segmentation [3,4], disease detection [5,6], and medical image synthesis [7][8][9].…”