# Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2107.00115 · HTML: https://mlsanity.com/paper/2107.00115/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 167 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 18 of 18)

- [Explainable deep learning models in medical image analysis](https://mlsanity.com/paper/2005.13799/) (2020)
- [A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability](https://mlsanity.com/paper/2008.09772/) (2020)
- [OCTID: Optical Coherence Tomography Image Database](https://mlsanity.com/paper/1812.07056/) (2018)
- [CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading](https://mlsanity.com/paper/1911.01376/) (2019)
- [Learn to Segment Retinal Lesions and Beyond](https://mlsanity.com/paper/1912.11619/) (2019)
- [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)
- [DRvertGRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images](https://mlsanity.com/paper/1910.11777/) (2019)
- [Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading](https://mlsanity.com/paper/1904.08764/) (2019)
- [Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis -- A Review](https://mlsanity.com/paper/1812.07101/) (2018)
- [An Ensemble Deep Learning Based Approach for Red Lesion Detection in Fundus Images](https://mlsanity.com/paper/1706.03008/) (2017)
- [Automatic segmentation of the Foveal Avascular Zone in ophthalmological OCT-A images](https://mlsanity.com/paper/1811.10374/) (2018)
- [Learning Mutually Local-global U-nets For High-resolution Retinal Lesion Segmentation in Fundus Images](https://mlsanity.com/paper/1901.06047/) (2019)
- [Predicting Risk of Developing Diabetic Retinopathy using Deep Learning](https://mlsanity.com/paper/2008.04370/) (2020)
- [Uncertainty aware and explainable diagnosis of retinal disease](https://mlsanity.com/paper/2101.12041/) (2021)
- [Microaneurysm Detection in Fundus Images Using a Two-step Convolutional Neural Networks](https://mlsanity.com/paper/1710.05191/) (2017)
- [Quantitative and Qualitative Evaluation of Explainable Deep Learning Methods for Ophthalmic Diagnosis](https://mlsanity.com/paper/2009.12648/) (2020)
- [Detection of Hard Exudates in Retinal Fundus Images using Deep Learning](https://mlsanity.com/paper/1808.03656/) (2018)

## Built on by (most cited first, 2 of 2)

- [An Improved Model for Diabetic Retinopathy Detection by using Transfer Learning and Ensemble Learning](https://mlsanity.com/paper/2308.05178/) (2023)
- [Looking into Concept Explanation Methods for Diabetic Retinopathy Classification](https://mlsanity.com/paper/2410.03188/) (2024)

## Abstract (arXiv)

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world,. In the past few Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, Artificial Intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss, Both fundus and optical coherence tomography (OCT) images are used to image the retina. With deep learning/machine learning apprroaches it is possible to extract features from the images and detect the presence of DR. Multiple strategies are implemented to detect and grade the presence of DR using classification, segmentation, and hybrid techniques. This review covers the literature dealing with AI approaches to DR that have been published in the open literature over a five year span (2016-2021). In addition a comprehensive list of available DR datasets is reported. Both the PICO (P-patient, I-intervention, C-control O-outcome) and Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA)2009 search strategies were employed. We summarize a total of 114 published articles which conformed to the scope of the review. In addition a list of 43 major datasets is presented.