# Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1705.00771 · HTML: https://mlsanity.com/paper/1705.00771/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2017.

## Built on by (most cited first, 16 of 16)

- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation](https://mlsanity.com/paper/2110.14160/) (2021)
- [Hierarchical Knowledge Guided Learning for Real-world Retinal Diseases Recognition](https://mlsanity.com/paper/2111.08913/) (2021)
- [SSiT: Saliency-guided Self-supervised Image Transformer for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2210.10969/) (2022)
- [Explainable Diabetic Retinopathy Detection and Retinal Image Generation](https://mlsanity.com/paper/2107.00296/) (2021)
- [SynFundus-1M: A High-quality Million-scale Synthetic fundus images Dataset with Fifteen Types of Annotation](https://mlsanity.com/paper/2312.00377/) (2023)
- [DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2212.14615/) (2022)
- [Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy](https://mlsanity.com/paper/2103.04207/) (2021)
- [A deep learning model for classification of diabetic retinopathy in eye fundus images based on retinal lesion detection](https://mlsanity.com/paper/2110.07745/) (2021)
- [Explainable and Interpretable Diabetic Retinopathy Classification Based on Neural-Symbolic Learning](https://mlsanity.com/paper/2204.00624/) (2022)
- [Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning](https://mlsanity.com/paper/2507.19199/) (2025)
- [A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening](https://mlsanity.com/paper/2208.01810/) (2022)
- [A Comparative Study of Filters and Deep Learning Models to predict Diabetic Retinopathy](https://mlsanity.com/paper/2309.15216/) (2023)
- [Diabetic Retinopathy Detection Using Quantum Transfer Learning](https://mlsanity.com/paper/2405.01734/) (2024)
- [Lesion-aware network for diabetic retinopathy diagnosis](https://mlsanity.com/paper/2408.07264/) (2024)
- [Learning to Fuse and Reconstruct Multi-View Graphs for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2602.21944/) (2026)

## Abstract (arXiv)

We propose an automatic diabetic retinopathy (DR) analysis algorithm based on two-stages deep convolutional neural networks (DCNN). Compared to existing DCNN-based DR detection methods, the proposed algorithm have the following advantages: (1) Our method can point out the location and type of lesions in the fundus images, as well as giving the severity grades of DR. Moreover, since retina lesions and DR severity appear with different scales in fundus images, the integration of both local and global networks learn more complete and specific features for DR analysis. (2) By introducing imbalanced weighting map, more attentions will be given to lesion patches for DR grading, which significantly improve the performance of the proposed algorithm. In this study, we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus images from Kaggle competition dataset. Under the guidance of clinical ophthalmologists, the experimental results show that our local lesion detection net achieve comparable performance with trained human observers, and the proposed imbalanced weighted scheme also be proved to significantly improve the capability of our DCNN-based DR grading algorithm.