# Deep Learning Approach to Diabetic Retinopathy Detection

arXiv: https://arxiv.org/abs/2003.02261 · HTML: https://mlsanity.com/paper/2003.02261/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks](https://mlsanity.com/paper/2109.01408/) (2021)
- [Detecting Severity of Diabetic Retinopathy from Fundus Images: A Transformer Network-based Review](https://mlsanity.com/paper/2301.00973/) (2023)
- [Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest Radiographs](https://mlsanity.com/paper/2202.03583/) (2022)
- [About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data Annotations](https://mlsanity.com/paper/2105.14117/) (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)
- [A Novel Adaptive Hybrid Focal-Entropy Loss for Enhancing Diabetic Retinopathy Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/2411.10843/) (2024)

## Abstract (arXiv)

Diabetic retinopathy is one of the most threatening complications of diabetes that leads to permanent blindness if left untreated. One of the essential challenges is early detection, which is very important for treatment success. Unfortunately, the exact identification of the diabetic retinopathy stage is notoriously tricky and requires expert human interpretation of fundus images. Simplification of the detection step is crucial and can help millions of people. Convolutional neural networks (CNN) have been successfully applied in many adjacent subjects, and for diagnosis of diabetic retinopathy itself. However, the high cost of big labeled datasets, as well as inconsistency between different doctors, impede the performance of these methods. In this paper, we propose an automatic deep-learning-based method for stage detection of diabetic retinopathy by single photography of the human fundus. Additionally, we propose the multistage approach to transfer learning, which makes use of similar datasets with different labeling. The presented method can be used as a screening method for early detection of diabetic retinopathy with sensitivity and specificity of 0.99 and is ranked 54 of 2943 competing methods (quadratic weighted kappa score of 0.925466) on APTOS 2019 Blindness Detection Dataset (13000 images).