# Deep Learning based Early Detection and Grading of Diabetic Retinopathy Using Retinal Fundus Images

arXiv: https://arxiv.org/abs/1812.10595 · HTML: https://mlsanity.com/paper/1812.10595/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [Learning Robust Representation for Joint Grading of Ophthalmic Diseases via Adaptive Curriculum and Feature Disentanglement](https://mlsanity.com/paper/2207.04183/) (2022)
- [XDR-LVLM: An Explainable Vision-Language Large Model for Diabetic Retinopathy Diagnosis](https://mlsanity.com/paper/2508.15168/) (2025)
- [Towards a Sample Efficient Reinforcement Learning Pipeline for Vision Based Robotics](https://mlsanity.com/paper/2105.09719/) (2021)
- [Convolutional Nets for Diabetic Retinopathy Screening in Bangladeshi Patients](https://mlsanity.com/paper/2108.04358/) (2021)
- [Designing the Architecture of a Convolutional Neural Network Automatically for Diabetic Retinopathy Diagnosis](https://mlsanity.com/paper/2110.03877/) (2021)
- [Comparing Conventional and Deep Feature Models for Classifying Fundus Photography of Hemorrhages](https://mlsanity.com/paper/2206.01118/) (2022)

## Abstract (arXiv)

Diabetic Retinopathy (DR) is a constantly deteriorating disease, being one of the leading causes of vision impairment and blindness. Subtle distinction among different grades and existence of many significant small features make the task of recognition very challenging. In addition, the present approach of retinopathy detection is a very laborious and time-intensive task, which heavily relies on the skill of a physician. Automated detection of diabetic retinopathy is essential to tackle these problems. Early-stage detection of diabetic retinopathy is also very important for diagnosis, which can prevent blindness with proper treatment. In this paper, we developed a novel deep convolutional neural network, which performs the early-stage detection by identifying all microaneurysms (MAs), the first signs of DR, along with correctly assigning labels to retinal fundus images which are graded into five categories. We have tested our network on the largest publicly available Kaggle diabetic retinopathy dataset, and achieved 0.851 quadratic weighted kappa score and 0.844 AUC score, which achieves the state-of-the-art performance on severity grading. In the early-stage detection, we have achieved a sensitivity of 98% and specificity of above 94%, which demonstrates the effectiveness of our proposed method. Our proposed architecture is at the same time very simple and efficient with respect to computational time and space are concerned.