# Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading

arXiv: https://arxiv.org/abs/1904.08764 · HTML: https://mlsanity.com/paper/1904.08764/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 12 of 12)

- [Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack](https://mlsanity.com/paper/2103.03438/) (2021)
- [Uncertainty-aware deep learning methods for robust diabetic retinopathy classification](https://mlsanity.com/paper/2201.09042/) (2022)
- [Conditional Alignment and Uniformity for Contrastive Learning with Continuous Proxy Labels](https://mlsanity.com/paper/2111.05643/) (2021)
- [Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey](https://mlsanity.com/paper/2107.00115/) (2021)
- [A ResNet is All You Need? Modeling A Strong Baseline for Detecting Referable Diabetic Retinopathy in Fundus Images](https://mlsanity.com/paper/2210.03180/) (2022)
- [Fully Automated Artery-Vein ratio and vascular tortuosity measurement in retinal fundus images](https://mlsanity.com/paper/2301.01791/) (2023)
- [Weed Recognition using Deep Learning Techniques on Class-imbalanced Imagery](https://mlsanity.com/paper/2112.07819/) (2021)
- [Co(ve)rtex: ML Models as storage channels and their (mis-)applications](https://mlsanity.com/paper/2307.08811/) (2023)
- [PoCo: A Self-Supervised Approach via Polar Transformation Based Progressive Contrastive Learning for Ophthalmic Disease Diagnosis](https://mlsanity.com/paper/2403.19124/) (2024)
- [FairEnc: A Fair Vision-Language Model with Fair Vision and Text Encoders for Glaucoma Detection](https://mlsanity.com/paper/2605.04882/) (2026)
- [PRISM-DR: Per-lesion Retinal Inference with Specialist Models for Diabetic Retinopathy](https://mlsanity.com/paper/2607.19864/) (2026)
- [Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning](https://mlsanity.com/paper/2608.06934/) (2026)

## Abstract (arXiv)

Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the human eye retina, the images of which are today used for manual disease screening. This labor-intensive task could greatly benefit from automatic detection using deep learning technique. Here we present a deep learning system that identifies referable diabetic retinopathy comparably or better than presented in the previous studies, although we use only a small fraction of images (<1/4) in training but are aided with higher image resolutions. We also provide novel results for five different screening and clinical grading systems for diabetic retinopathy and macular edema classification, including results for accurately classifying images according to clinical five-grade diabetic retinopathy and four-grade diabetic macular edema scales. These results suggest, that a deep learning system could increase the cost-effectiveness of screening while attaining higher than recommended performance, and that the system could be applied in clinical examinations requiring finer grading.