# Dual Branch Deep Learning Network for Detection and Stage Grading of Diabetic Retinopathy

arXiv: https://arxiv.org/abs/2308.09945 · HTML: https://mlsanity.com/paper/2308.09945/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 19 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy](https://mlsanity.com/paper/1710.01711/) (2017)
- [Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction](https://mlsanity.com/paper/2006.00197/) (2020)
- [Imbalanced Image Classification with Complement Cross Entropy](https://mlsanity.com/paper/2009.02189/) (2020)

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

- [Integrating Non-Linear Radon Transformation for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2504.15883/) (2025)
- [Enhancing DR Classification with Swin Transformer and Shifted Window Attention](https://mlsanity.com/paper/2504.15317/) (2025)
- [VR-FuseNet: A Fusion of Heterogeneous Fundus Data and Explainable Deep Network for Diabetic Retinopathy Classification](https://mlsanity.com/paper/2504.21464/) (2025)
- [Enhancing Diabetic Retinopathy Detection with CNN-Based Models: A Comparative Study of UNET and Stacked UNET Architectures](https://mlsanity.com/paper/2411.01251/) (2024)
- [From Retinal Pixels to Patients: Evolution of Deep Learning Research in Diabetic Retinopathy Screening](https://mlsanity.com/paper/2511.11065/) (2025)
- [Dual-Resolution Attention-Gated Deep Learning with Ordinal Regression for Diabetic Retinopathy Grading: A Quantified Assessment of Cross-Domain Generalization](https://mlsanity.com/paper/2604.17341/) (2026)
- [Multi-Channel Feature Fusion and Monte Carlo Dropout for Uncertainty-Aware Diabetic Retinopathy Grading](https://mlsanity.com/paper/2608.15234/) (2026)

## Abstract (arXiv)

Diabetic retinopathy is a severe complication of diabetes that can lead to permanent blindness if not treated promptly. Early and accurate diagnosis of the disease is essential for successful treatment. This paper introduces a deep learning method for the detection and stage grading of diabetic retinopathy, using a single fundus retinal image. Our model utilizes transfer learning, employing two state-of-the-art pre-trained models as feature extractors and fine-tuning them on a new dataset. The proposed model is trained on a large multi-center dataset, including the APTOS 2019 dataset, obtained from publicly available sources. It achieves remarkable performance in diabetic retinopathy detection and stage classification on the APTOS 2019, outperforming the established literature. For binary classification, the proposed approach achieves an accuracy of 98.50, a sensitivity of 99.46, and a specificity of 97.51. In stage grading, it achieves a quadratic weighted kappa of 93.00, an accuracy of 89.60, a sensitivity of 89.60, and a specificity of 97.72. The proposed approach serves as a reliable screening and stage grading tool for diabetic retinopathy, offering significant potential to enhance clinical decision-making and patient care.