Diabetic retinopathy grading review: Current techniques and future directions.

Diabetic retinopathy (DR) is widely recognized as a leading cause of blindness among individuals with diabetes worldwide. Therefore, early diagnosis of DR plays a crucial role in preserving patients' vision and halting the progression of the disease to advanced stages. However, manual diagnosis of DR in clinical practice is time-consuming and susceptible to human error, especially during the early stages when the lesions associated with DR are often minute and challenging to identify. Furthermore, with the projected surge in the number of diabetic patients and a concurrent shortage of ophthalmologists, there will be insufficient healthcare professionals available to examine all individuals at risk. The application of machine- and deep learning-based techniques has proven effective in diagnosing medical images, including those related to DR. In this review, we surveyed and analyzed 55 DR grading studies published between 2018 and 2022 extracted from four academic digital libraries: Scopus, Web of Science, Google Scholar, and Science Direct. The review provides a comprehensive discussion and analysis of these selected studies, considering various aspects such as benchmark DR datasets, classification tasks, preprocessing techniques, learning approaches, and performance evaluation measures. Within the literature on DR grading, supervised-based learning techniques have been found to be more prevalent than semi-supervised learning techniques. Furthermore, researchers predominantly addressed this problem as an image-level classification task, overlooking the distinctive characteristics of lesions within each grade. Numerous proposed techniques primarily concentrate on detecting the early stages of DR, while a limited number of studies address the disease's advanced stages. The primary findings of our analysis reveal a potential direction for future research that emphasizes data- and model-centric approaches. • Proposing a taxonomy for categorizing the literature in DR grading. • Evaluating the DR benchmark datasets and highlighting their drawbacks. • Exploring various image processing methods that enhance DR models' performance. • Comparing different learning techniques used to develop DR grading models. • Highlighting open research gaps toward reliable DR grading approaches. [ABSTRACT FROM AUTHOR]