Diabetic retinopathy screening using machine learning: a systematic review.

Abstract

Diabetic retinopathy (DR) stands as a leading cause of global blindness. Early identification and prompt treatment are crucial in preventing vision impairment caused by diabetic retinopathy (DR). Manual screening of retinal fundus images is challenging and time-consuming. Additionally, there is a significant gap between the number of DR patients and the number of medical experts. Integrating machine learning (ML) and deep learning (DL) techniques is becoming a viable alternative to traditional DR screening techniques. However, the absence of a retinal dataset with standardized quality, the complexity of DL models, and the need for high computational resources are challenges. Therefore, in this study, we studied and analyzed the research landscape in integrating ML techniques in DR screening. In this regard, our work contributes significantly in several aspects. Initially, we identify and characterize images of the retinal fundus that are readily available. Then, we discuss commonly used preprocessing techniques in DR screening. In addition, we analyze the progress of ML techniques in DR screening. Lastly, we discussed existing challenges and showed future directions.