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

arXiv: https://arxiv.org/abs/2208.01810 · HTML: https://mlsanity.com/paper/2208.01810/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 305 references that are in the map.
- First posted on arXiv 3 Aug 2022.

## Builds on (most cited first, 28 of 28)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Deep Retinal Image Understanding](https://mlsanity.com/paper/1609.01103/) (2016)
- [Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection](https://mlsanity.com/paper/1706.04372/) (2017)
- [Deep image mining for diabetic retinopathy screening](https://mlsanity.com/paper/1610.07086/) (2016)
- [Retinal vessel segmentation based on Fully Convolutional Neural Networks](https://mlsanity.com/paper/1812.07110/) (2018)
- [Deep Learning based Computer-Aided Diagnosis Systems for Diabetic Retinopathy: A Survey](https://mlsanity.com/paper/1811.01238/) (2018)
- [Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images](https://mlsanity.com/paper/1706.09634/) (2017)
- [Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network](https://mlsanity.com/paper/1704.00979/) (2017)
- [Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks](https://mlsanity.com/paper/1705.00771/) (2017)
- [A Fully Convolutional Neural Network based Structured Prediction Approach Towards the Retinal Vessel Segmentation](https://mlsanity.com/paper/1611.02064/) (2016)
- [An Ensemble Deep Learning Based Approach for Red Lesion Detection in Fundus Images](https://mlsanity.com/paper/1706.03008/) (2017)
- [An Ensemble-based System for Microaneurysm Detection and Diabetic Retinopathy Grading](https://mlsanity.com/paper/1410.8577/) (2014)
- [Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography](https://mlsanity.com/paper/1609.05871/) (2016)
- [Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network](https://mlsanity.com/paper/1702.00509/) (2017)
- [Automated Detection of Microaneurysms in Color Fundus Images using Deep Learning with Different Preprocessing Approaches](https://mlsanity.com/paper/2004.09493/) (2020)
- [Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images](https://mlsanity.com/paper/1603.04833/) (2016)
- [Retinal Microaneurysms Detection using Local Convergence Index Features](https://mlsanity.com/paper/1707.06865/) (2017)
- [Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy](https://mlsanity.com/paper/1710.10675/) (2017)
- [Detection of Hard Exudates in Retinal Fundus Images using Deep Learning](https://mlsanity.com/paper/1808.03656/) (2018)
- [A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy](https://mlsanity.com/paper/1909.01557/) (2019)
- [Computationally Efficient Optic Nerve Head Detection in Retinal Fundus Images](https://mlsanity.com/paper/1909.01558/) (2019)
- [Comparison Different Vessel Segmentation Methods in Automated Microaneurysms Detection in Retinal Images using Convolutional Neural Networks](https://mlsanity.com/paper/2005.09097/) (2020)
- [The Efficacy of Microaneurysms Detection With and Without Vessel Segmentation in Color Retinal Images](https://mlsanity.com/paper/2005.09098/) (2020)
- [Improved Microaneurysm Detection using Deep Neural Networks](https://mlsanity.com/paper/1505.04424/) (2015)
- [Classification of Diabetic Retinopathy Images Using Multi-Class Multiple-Instance Learning Based on Color Correlogram Features](https://mlsanity.com/paper/1704.01264/) (2017)
- [Automated Fovea Detection Based on Unsupervised Retinal Vessel Segmentation Method](https://mlsanity.com/paper/2004.08540/) (2020)
- [Automated Optic Nerve Head Detection Based on Different Retinal Vasculature Segmentation Methods and Mathematical Morphology](https://mlsanity.com/paper/2004.10253/) (2020)

## Abstract (arXiv)

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.