# FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Grading

arXiv: https://arxiv.org/abs/2205.01676 · HTML: https://mlsanity.com/paper/2205.01676/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 2 May 2022.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Meta Pseudo Labels](https://mlsanity.com/paper/2003.10580/) (2020)
- [REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs](https://mlsanity.com/paper/1910.03667/) (2019)
- [Pre-training without Natural Images](https://mlsanity.com/paper/2101.08515/) (2021)
- [Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations](https://mlsanity.com/paper/2007.07423/) (2020)
- [Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces](https://mlsanity.com/paper/1907.05345/) (2019)
- [Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning](https://mlsanity.com/paper/2011.05231/) (2020)

## Built on by (most cited first, 9 of 9)

- [Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysis](https://mlsanity.com/paper/2410.24046/) (2024)
- [LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images](https://mlsanity.com/paper/2309.05780/) (2023)
- [PVBM: A Python Vasculature Biomarker Toolbox Based On Retinal Blood Vessel Segmentation](https://mlsanity.com/paper/2208.00392/) (2022)
- [VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images](https://mlsanity.com/paper/2409.16016/) (2024)
- [GONet: A Generalizable Deep Learning Model for Glaucoma Detection](https://mlsanity.com/paper/2502.19514/) (2025)
- [Lirot.ai: A Novel Platform for Crowd-Sourcing Retinal Image Segmentations](https://mlsanity.com/paper/2208.10100/) (2022)
- [Fundus Image-based Visual Acuity Assessment with PAC-Guarantees](https://mlsanity.com/paper/2412.06624/) (2024)
- [FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment](https://mlsanity.com/paper/2506.20303/) (2025)
- [Semi-Supervised Multi-Task Learning for Interpretable Quality As- sessment of Fundus Images](https://mlsanity.com/paper/2511.13353/) (2025)

## Abstract (arXiv)

Objective: Ophthalmological pathologies such as glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and vision impairment. There is a need for novel decision support tools that can simplify and speed up the diagnosis of these pathologies. A key step in this process is to automatically estimate the quality of the fundus images to make sure these are interpretable by a human operator or a machine learning model. We present a novel fundus image quality scale and deep learning (DL) model that can estimate fundus image quality relative to this new scale. Methods: A total of 1,245 images were graded for quality by two ophthalmologists within the range 1-10, with a resolution of 0.5. A DL regression model was trained for fundus image quality assessment. The architecture used was Inception-V3. The model was developed using a total of 89,947 images from 6 databases, of which 1,245 were labeled by the specialists and the remaining 88,702 images were used for pre-training and semi-supervised learning. The final DL model was evaluated on an internal test set (n=209) as well as an external test set (n=194). Results: The final DL model, denoted FundusQ-Net, achieved a mean absolute error of 0.61 (0.54-0.68) on the internal test set. When evaluated as a binary classification model on the public DRIMDB database as an external test set the model obtained an accuracy of 99%. Significance: the proposed algorithm provides a new robust tool for automated quality grading of fundus images.