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

Tabik, S.; G'omez-R'ios, A.; Mart'in-Rodr'iguez, J. L.; Sevillano-Garc'ia, I.; Rey-Area, M.; Charte, D.; Guirado, E.; Su'arez, J. L.; Luengo, J.; Valero-Gonz'alez, M. A.; Garc'ia-Villanova, P.; Olmedo-S'anchez, E.; Herrera, F.

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 triage systems for 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 377 positive and 377 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 $97.37\% \pm 1.86 \%$, $88.14\% \pm 2.02\%$, $66.5\% \pm 8.04\%$ in severe, moderate and mild COVID severity levels. Our approach could help in the early detection of COVID-19. COVIDGR-1.0 dataset will be made available after the review process.