COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images

By Siham Tabik, A. G'omez-R'ios, J. L. Martín-Rodríguez, I. Sevillano-García, M. Rey-Area, David Charte, Emilio Guirado, J. L. Suarez, J. Luengo, M. A. Valero-Gonz'alez and 3 more. IEEE journal of biomedical and health informatics, 2020

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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 article 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 Clínico 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 <inline-formula><tex-math notation="LaTeX">$\text{97.72}\% \pm \text{0.95}\%$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{86.90}\% \pm \text{3.20}\%$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{61.80}\% \pm \text{5.49}\%$</tex-math></inline-formula> in severe, moderate and mild COVID-19 severity levels. 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 <uri>https://dasci.es/es/transferencia/open-data/covidgr/</uri>.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b2cd097b7e4a5425919b2c519c34cbe8c0049806

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In Artificial Intelligence

  1. Yudong Zhang
  2. Shui-hua Wang
  3. Arash Bahrammirzaee

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