# Deep Learning based Computer-Aided Diagnosis Systems for Diabetic Retinopathy: A Survey

arXiv: https://arxiv.org/abs/1811.01238 · HTML: https://mlsanity.com/paper/1811.01238/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2018.

## Built on by (most cited first, 19 of 19)

- [Exploring the Transfer Learning Capabilities of CLIP in Domain Generalization for Diabetic Retinopathy](https://mlsanity.com/paper/2308.14212/) (2023)
- [Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging](https://mlsanity.com/paper/2303.13033/) (2023)
- [MVCNet: Multiview Contrastive Network for Unsupervised Representation Learning for 3D CT Lesions](https://mlsanity.com/paper/2108.07662/) (2021)
- [Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models](https://mlsanity.com/paper/2208.11563/) (2022)
- [A Sub-pixel Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis](https://mlsanity.com/paper/2205.09315/) (2022)
- [IKD+: Reliable Low Complexity Deep Models For Retinopathy Classification](https://mlsanity.com/paper/2303.02310/) (2023)
- [Advances in Classifying the Stages of Diabetic Retinopathy Using Convolutional Neural Networks in Low Memory Edge Devices](https://mlsanity.com/paper/2106.01739/) (2021)
- [Detecting Severity of Diabetic Retinopathy from Fundus Images: A Transformer Network-based Review](https://mlsanity.com/paper/2301.00973/) (2023)
- [Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks](https://mlsanity.com/paper/2105.03995/) (2021)
- [A systematic review of transfer learning based approaches for diabetic retinopathy detection](https://mlsanity.com/paper/2105.13793/) (2021)
- [A Deep Learning-Based Unified Framework for Red Lesions Detection on Retinal Fundus Images](https://mlsanity.com/paper/2109.05021/) (2021)
- [Deep Semi-Supervised and Self-Supervised Learning for Diabetic Retinopathy Detection](https://mlsanity.com/paper/2208.02408/) (2022)
- [Designing the Architecture of a Convolutional Neural Network Automatically for Diabetic Retinopathy Diagnosis](https://mlsanity.com/paper/2110.03877/) (2021)
- [A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening](https://mlsanity.com/paper/2208.01810/) (2022)
- [Explainable Artificial Intelligence in Retinal Imaging for the detection of Systemic Diseases](https://mlsanity.com/paper/2212.07058/) (2022)
- [A Global and Patch-wise Contrastive Loss for Accurate Automated Exudate Detection](https://mlsanity.com/paper/2302.11517/) (2023)
- [Artificial Intelligence and Diabetes Mellitus: An Inside Look Through the Retina](https://mlsanity.com/paper/2402.18600/) (2024)
- [DRetNet: A Novel Deep Learning Framework for Diabetic Retinopathy Diagnosis](https://mlsanity.com/paper/2509.01072/) (2025)
- [PRISM-DR: Per-lesion Retinal Inference with Specialist Models for Diabetic Retinopathy](https://mlsanity.com/paper/2607.19864/) (2026)

## Abstract (arXiv)

Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided diagnosis (CAD) system based on retinal fundus images is an efficient and effective method for early DR diagnosis and assisting experts. A computer-aided diagnosis (CAD) system involves various stages like detection, segmentation and classification of lesions in fundus images. Many traditional machine-learning (ML) techniques based on hand-engineered features have been introduced. The recent emergence of deep learning (DL) and its decisive victory over traditional ML methods for various applications motivated the researchers to employ it for DR diagnosis, and many deep-learning-based methods have been introduced. In this paper, we review these methods, highlighting their pros and cons. In addition, we point out the challenges to be addressed in designing and learning about efficient, effective and robust deep-learning algorithms for various problems in DR diagnosis and draw attention to directions for future research.