A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening

TL;DR

This survey reviews the current state of computer-aided diagnostic systems for diabetic retinopathy, highlighting recent algorithms, databases, and challenges in retinal image analysis to improve early detection.

Contribution

It provides a comprehensive overview of CAD systems in diabetic retinopathy screening, emphasizing recent advances, databases, and algorithmic frameworks for researchers and practitioners.

Findings

Recent algorithms improve detection accuracy

Databases vary in size and quality

Challenges include image variability and algorithm robustness

Abstract

Diabetes Mellitus (DM) can lead to significant microvasculature disruptions that eventually causes diabetic retinopathy (DR), or complications in the eye due to diabetes. If left unchecked, this disease can increase over time and eventually cause complete vision loss. The general method to detect such optical developments is through examining the vessels, optic nerve head, microaneurysms, haemorrhage, exudates, etc. from retinal images. Ultimately this is limited by the number of experienced ophthalmologists and the vastly growing number of DM cases. To enable earlier and efficient DR diagnosis, the field of ophthalmology requires robust computer aided diagnosis (CAD) systems. Our review is intended for anyone, from student to established researcher, who wants to understand what can be accomplished with CAD systems and their algorithms to modeling and where the field of retinal image processing in computer vision and pattern recognition is headed. For someone just getting started, we place a special emphasis on the logic, strengths and shortcomings of different databases and algorithms frameworks with a focus on very recent approaches.