FGR-Net: Interpretable fundus image gradeability classification based on deep reconstruction learning

Saif Khalid, Hatem A. Rashwan, Saddam Abdulwahab, Mohamed Abdel-Nasser, Facundo Manuel Quiroga, Domenec Puig

Abstract: Highlights•FGR-NET is a deep learning model to assess fundus image gradeability.•FGR-NET consists of two parallel deep networks: autoencoder and classifier.•Using self-supervised learning, autoencoder is to extract the images’ key features.•A classifier is to distinguish between gradable and ungradable fundus images.•Interpretability is performed to understand how FGR-NET evaluates the gradeability.