Automatic Detection of AMD and DME Retinal Pathologies Using Deep Learning
Latifa Saidi, Hajer Jomaa, Haddad Zainab, Hsouna Zgolli, Sonia Mabrouk, Désiré Sidibé, Hedi Tabia, Nawrès Khlifa
Abstract
Diabetic macular edema (DME) and age-related macular degeneration (AMD) are two common eye diseases. They are often undiagnosed or diagnosed late. This can result in permanent and irreversible vision loss. Therefore, early detection and treatment of these diseases can prevent vision loss, save money, and provide a better quality of life for individuals. Optical coherence tomography (OCT) imaging is widely applied to identify eye diseases, including DME and AMD. In this work, we developed automatic deep learning-based methods to detect these pathologies using SD-OCT scans. The convolutional neural network (CNN) from scratch we developed gave the best classification score with an accuracy higher than 99% on Duke dataset of OCT images.