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

• URL: http://arxiv.org/abs/2006.01409v3
• Date: Wed, 11 Nov 2020 06:15:00 GMT
• Title: COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images
• Authors: S. Tabik, A. G\'omez-R\'ios, J.L. Mart\'in-Rodr\'iguez, I. Sevillano-Garc\'ia, M. Rey-Area, D. Charte, E. Guirado, J.L. Su\'arez, J. Luengo, M.A. Valero-Gonz\'alez, P. Garc\'ia-Villanova, E. Olmedo-S\'anchez, F. Herrera
• Abstract summary: Currently, Coronavirus disease (COVID-19) is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. Deep learning neural networks have a great potential for building COVID-19 triage systems. 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, and (iii) we propose COVID Smart Data based Network (COVID-SD
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $97.72\% \pm 0.95 \%$, $86.90\% \pm 3.20\%$, $61.80\% \pm 5.49\%$ 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/.

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