# COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images

arXiv: https://arxiv.org/abs/2006.01409 · HTML: https://mlsanity.com/paper/2006.01409/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

## Built on by (most cited first, 30 of 32)

- [OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM](https://mlsanity.com/paper/2402.09181/) (2024)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection](https://mlsanity.com/paper/2203.15269/) (2022)
- [PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children](https://mlsanity.com/paper/2203.10612/) (2022)
- [Self-supervised deep convolutional neural network for chest X-ray classification](https://mlsanity.com/paper/2103.03055/) (2021)
- [Improving Representation of High-frequency Components for Medical Visual Foundation Models](https://mlsanity.com/paper/2407.14651/) (2024)
- [Vision Transformers in Medical Imaging: A Review](https://mlsanity.com/paper/2211.10043/) (2022)
- [A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Images](https://mlsanity.com/paper/2110.06340/) (2021)
- [Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels](https://mlsanity.com/paper/2202.07422/) (2022)
- [Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM](https://mlsanity.com/paper/2203.14275/) (2022)
- [COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers](https://mlsanity.com/paper/2207.09529/) (2022)
- [DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification](https://mlsanity.com/paper/2308.00475/) (2023)
- [MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification](https://mlsanity.com/paper/2401.00728/) (2024)
- [DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training](https://mlsanity.com/paper/2407.11594/) (2024)
- [MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging](https://mlsanity.com/paper/2604.13756/) (2026)
- [A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging](https://mlsanity.com/paper/2108.04344/) (2021)
- [MobileCaps: A Lightweight Model for Screening and Severity Analysis of COVID-19 Chest X-Ray Images](https://mlsanity.com/paper/2108.08775/) (2021)
- [Secure Federated Learning Approaches to Diagnosing COVID-19](https://mlsanity.com/paper/2401.12438/) (2024)
- [CNN-based explanation ensembling for dataset, representation and explanations evaluation](https://mlsanity.com/paper/2404.10387/) (2024)
- [Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging](https://mlsanity.com/paper/2412.20070/) (2024)
- [Exploring the Capabilities of Large Language Model Encoders for Image-Text Retrieval in Chest X-rays](https://mlsanity.com/paper/2509.15234/) (2025)
- [Tiled sparse coding in eigenspaces for the COVID-19 diagnosis in chest X-ray images](https://mlsanity.com/paper/2106.14724/) (2021)
- [A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training](https://mlsanity.com/paper/2109.03748/) (2021)
- [Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images](https://mlsanity.com/paper/2208.01637/) (2022)
- [COVID-19 Classification Using Deep Learning Two-Stage Approach](https://mlsanity.com/paper/2211.15817/) (2022)
- [COVID-VR: A Deep Learning COVID-19 Classification Model Using Volume-Rendered Computer Tomography](https://mlsanity.com/paper/2308.01433/) (2023)
- [CoVScreen: Pitfalls and recommendations for screening COVID-19 using Chest X-rays](https://mlsanity.com/paper/2405.07674/) (2024)
- [InfLocNet: Enhanced Lung Infection Localization and Disease Detection from Chest X-Ray Images Using Lightweight Deep Learning](https://mlsanity.com/paper/2408.06459/) (2024)
- [From Lab to Pocket: A Novel Continual Learning-based Mobile Application for Screening COVID-19](https://mlsanity.com/paper/2410.12589/) (2024)
- [Efficient Semi-Supervised Adversarial Training via Latent Clustering-Based Data Reduction](https://mlsanity.com/paper/2501.10466/) (2025)

## Abstract (arXiv)

Currently, Coronavirus disease (COVID-19), one of the most infectious diseases in the 21st century, is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. CT (Computed Tomography) scanners and RT-PCR testing are not available in most medical centers and hence in many cases CXR images become the most time/cost effective tool for assisting clinicians in making decisions. Deep learning neural networks have a great potential for building COVID-19 triage systems and detecting COVID-19 patients, especially patients with low severity. Unfortunately, current databases do not allow building such systems as they are highly heterogeneous and biased towards severe cases. This paper is three-fold: (i) we demystify the high sensitivities achieved by most recent COVID-19 classification models, (ii) under a close collaboration with Hospital Universitario Cl'inico San Cecilio, Granada, Spain, we built COVIDGR-1.0, a homogeneous and balanced database that includes all levels of severity, from normal with Positive RT-PCR, Mild, Moderate to Severe. COVIDGR-1.0 contains 426 positive and 426 negative PA (PosteroAnterior) CXR views and (iii) we propose COVID Smart Data based Network (COVID-SDNet) methodology for improving the generalization capacity of COVID-classification models. Our approach reaches good and stable results with an accuracy of 97.72% ± 0.95 %, 86.90% ± 3.20%, 61.80% ± 5.49% in severe, moderate and mild COVID-19 severity levels (Paper accepted for publication in Journal of Biomedical and Health Informatics). Our approach could help in the early detection of COVID-19. COVIDGR-1.0 along with the severity level labels are available to the scientific community through this link https://dasci.es/es/transferencia/open-data/covidgr/.