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Currently, Coronavirus disease (COVID-19), one of the most infectiousdiseases in the 21st century, is diagnosed using RT-PCR testing, CT scansand/or Chest X-Ray (CXR) images. CT (Computed Tomography) scanners and RT-PCRtesting are not available in most medical centers and hence in many cases CXRimages become the most time/cost effective tool for assisting clinicians inmaking decisions. Deep learning neural networks have a great potential forbuilding COVID-19 triage systems and detecting COVID-19 patients, especiallypatients with low severity. Unfortunately, current databases do not allowbuilding such systems as they are highly heterogeneous and biased towardssevere cases. This paper is three-fold: (i) we demystify the high sensitivitiesachieved by most recent COVID-19 classification models, (ii) under a closecollaboration with Hospital Universitario Cl\'inico San Cecilio, Granada,Spain, we built COVIDGR-1.0, a homogeneous and balanced database that includesall levels of severity, from normal with Positive RT-PCR, Mild, Moderate toSevere. 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-classificationmodels. 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 inJournal of Biomedical and Health Informatics). Our approach could help in theearly detection of COVID-19. COVIDGR-1.0 along with the severity level labelsare available to the scientific community through this linkhttps://dasci.es/es/transferencia/open-data/covidgr/.

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