# A Deep Learning-Based Unified Framework for Red Lesions Detection on Retinal Fundus Images

arXiv: https://arxiv.org/abs/2109.05021 · HTML: https://mlsanity.com/paper/2109.05021/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?](https://mlsanity.com/paper/1706.00712/) (2017)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [An Analysis of Scale Invariance in Object Detection - SNIP](https://mlsanity.com/paper/1711.08189/) (2017)
- [MatConvNet - Convolutional Neural Networks for MATLAB](https://mlsanity.com/paper/1412.4564/) (2014)
- [Deep Retinal Image Understanding](https://mlsanity.com/paper/1609.01103/) (2016)
- [Scale-aware Fast R-CNN for Pedestrian Detection](https://mlsanity.com/paper/1510.08160/) (2015)
- [Deep Learning based Computer-Aided Diagnosis Systems for Diabetic Retinopathy: A Survey](https://mlsanity.com/paper/1811.01238/) (2018)
- [An Ensemble Deep Learning Based Approach for Red Lesion Detection in Fundus Images](https://mlsanity.com/paper/1706.03008/) (2017)
- [Microaneurysm Detection in Fundus Images Using a Two-step Convolutional Neural Networks](https://mlsanity.com/paper/1710.05191/) (2017)
- [Self-Transfer Learning for Fully Weakly Supervised Object Localization](https://mlsanity.com/paper/1602.01625/) (2016)

## Built on by (most cited first, 1 of 1)

- [PRISM-DR: Per-lesion Retinal Inference with Specialist Models for Diabetic Retinopathy](https://mlsanity.com/paper/2607.19864/) (2026)

## Abstract (arXiv)

Red-lesions, microaneurysms (MAs) and hemorrhages (HMs), are the early signs of diabetic retinopathy (DR). The automatic detection of MAs and HMs on retinal fundus images is a challenging task. Most of the existing methods detect either only MAs or only HMs because of the difference in their texture, sizes, and morphology. Though some methods detect both MAs and HMs, they suffer from the curse of dimensionality of shape and colors features and fail to detect all shape variations of HMs such as flame-shaped. Leveraging the progress in deep learning, we proposed a two-stream red lesions detection system dealing simultaneously with small and large red lesions. For this system, we introduced a new ROIs candidates generation method for large red lesions on fundus images; it is based on blood vessel segmentation and morphological operations, and reduces the computational complexity, and enhances the detection accuracy by generating a small number of potential candidates. For detection, we proposed a framework with two streams. We used pretrained VGGNet as a backbone model and carried out several extensive experiments to tune it for vessels segmentation and candidates generation, and finally learning the appropriate mapping, which yields better detection of the red lesions comparing with the state-of-the-art methods. The experimental results validated the effectiveness of the system in the detection of both MAs and HMs; it yields higher performance for per lesion detection; its sensitivity equals 0.8589 and good FROC score under 8 FPIs on DiaretDB1-MA reports FROC=0.7518, and with SN=0.7552 and good FROC score under 2,4and 8 FPIs on DiaretDB1-HM, and SN=0.8157 on e-ophtha with overall FROC=0.4537 and on ROCh dataset with FROC=0.3461 which is higher than the state-of-the art methods. For DR screening, the system performs well with good AUC on DiaretDB1-MA, DiaretDB1-HM, and e-ophtha datasets.