mlsanityData to 7 Oct 2026

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

arXiv 2006.01409 ↗First posted 2 Jun 2020eess.IV cs.CVcited by 32

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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/.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jun 2020.

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Citing papers per year: 2021: 7; 2022: 8; 2023: 2; 2024: 10; 2025: 3; 2026: 2.

32 papers.

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  1. 2024OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLMcited by 182
  2. 2021Deep Learning for Chest X-ray Analysis: A Surveycited by 84
  3. 2022Vision Transformers in Medical Computer Vision -- A Contemplative Retrospectioncited by 22
  4. 2022PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in childrencited by 19
  5. 2021Self-supervised deep convolutional neural network for chest X-ray classificationcited by 16
  6. 2022Vision Transformers in Medical Imaging: A Reviewcited by 8
  7. 2024Improving Representation of High-frequency Components for Medical Visual Foundation Modelscited by 8
  8. 2021A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Imagescited by 6
  9. 2022Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labelscited by 5
  10. 2022Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBMcited by 4
  11. 2022COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformerscited by 4
  12. 2023DINO-CXR: A self supervised method based on vision transformer for chest X-ray classificationcited by 3
20 more
  1. 2024MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classificationcited by 2
  2. 2024DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Trainingcited by 2
  3. 2026MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imagingcited by 2
  4. 2021A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imagingcited by 1
  5. 2021MobileCaps: A Lightweight Model for Screening and Severity Analysis of COVID-19 Chest X-Ray Imagescited by 1
  6. 2024Secure Federated Learning Approaches to Diagnosing COVID-19cited by 1
  7. 2024CNN-based explanation ensembling for dataset, representation and explanations evaluationcited by 1
  8. 2024Exploring Compositional Generalization of Multimodal LLMs for Medical Imagingcited by 1
  9. 2025Exploring the Capabilities of Large Language Model Encoders for Image-Text Retrieval in Chest X-rayscited by 1
  10. 2021Tiled sparse coding in eigenspaces for the COVID-19 diagnosis in chest X-ray imagescited by 0
  11. 2021A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during trainingcited by 0
  12. 2022Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Imagescited by 0
  13. 2022COVID-19 Classification Using Deep Learning Two-Stage Approachcited by 0
  14. 2023COVID-VR: A Deep Learning COVID-19 Classification Model Using Volume-Rendered Computer Tomographycited by 0
  15. 2024CoVScreen: Pitfalls and recommendations for screening COVID-19 using Chest X-rayscited by 0
  16. 2024InfLocNet: Enhanced Lung Infection Localization and Disease Detection from Chest X-Ray Images Using Lightweight Deep Learningcited by 0
  17. 2024From Lab to Pocket: A Novel Continual Learning-based Mobile Application for Screening COVID-19cited by 0
  18. 2025Efficient Semi-Supervised Adversarial Training via Latent Clustering-Based Data Reductioncited by 0
  19. 2025Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN Architecturescited by 0
  20. 2026Are Data Augmentation and Segmentation Always Necessary? Insights from COVID-19 X-Rays and a Methodology Thereofcited by 0