Deep learning for diabetic retinopathy detection and classification based on fundus images: A review
Nikos Tsiknakis, Dimitris Theodoropoulos, Georgios C. Manikis, Emmanouil Ktistakis, Ourania Boutsora, Alexa Bertó, Fábio Scarpa, Alberto Scarpa, Dimitrios I. Fotiadis, Kostas Marias
Abstract
Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions.