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; Nawres Khlifa

doi:10.1155/2023/9966107

Automatic Detection of AMD and DME Retinal Pathologies Using Deep Learning

Int J Biomed Imaging. 2023 Nov 24:2023:9966107. doi: 10.1155/2023/9966107. eCollection 2023.

Authors

Latifa Saidi¹, Hajer Jomaa¹, Haddad Zainab², Hsouna Zgolli³, Sonia Mabrouk³, Désiré Sidibé⁴, Hedi Tabia⁴, Nawres Khlifa¹

Affiliations

¹ Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, Tunis, Tunisia.
² Laboratory of Biophysics and Medical Technologies, National Engineering School Tunis, University of Tunis El Manar, Tunis, Tunisia.
³ Department A, Hedi Raies of Ophthalmology Institute, Tunis, Tunisia.
⁴ IBISC, University of Paris-Saclay, Univ Evry, Evry, France.

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.