Detection and Grading of Diabetic Retinopathy in Retinal Images Using Deep Intelligent Systems: A Comprehensive Review

Abstract

Diabetic Retinopathy (DR) is an eye disease that mainly affects people with diabetes. People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage. Once the vision is lost, it cannot be regained but can be prevented from causing any further damage. Early diagnosis of DR is required for preventing vision loss, for which a trained ophthalmologist is required. The clinical practice is time-consuming and is not much successful in identifying DR at early stages. Hence, Computer-Aided Diagnosis (CAD) system is a suitable alternative for screening and grading of DR for a larger population. This paper addresses the different stages in CAD system and the challenges in identifying and grading of DR by analyzing various recently evolved techniques. The performance metrics used to evaluate the Computer-Aided Diagnosis system for clinical practice is also discussed.

A review on supervised learning methodologies for detection of exudates in diabetic retinopathy

“…Manual diagnosis of DR may give inaccurate results. Computer aided screening of DR could be helpful for ophthalmologists in providing quick and precise diagnosis [2]- [4]. Though, therapeutic options such as pharmacotherapy are being made available along with traditional laser therapy [5].…”