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 $\text{97.72}\% \pm \text{0.95}\%$ , $\text{86.90}\% \pm \text{3.20}\%$ , $\text{61.80}\% \pm \text{5.49}\%$ 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 https://dasci.es/es/transferencia/open-data/covidgr/ .

中文翻译：

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基于胸部 X 射线图像预测 COVID-19 的 COVIDGR 数据集和 COVID-SDNet 方法

目前，冠状病毒病 (COVID-19) 是 21 世纪最具传染性的疾病之一，可通过 RT-PCR 检测、CT 扫描和/或胸部 X 光 (CXR) 图像进行诊断。大多数医疗中心都没有 CT（计算机断层扫描）扫描仪和 RT-PCR 检测，因此在许多情况下 CXR 图像成为协助临床医生做出决策的最省时/最具成本效益的工具。深度学习神经网络在构建 COVID-19 分诊系统和检测 COVID-19 患者，特别是严重程度较低的患者方面具有巨大潜力。不幸的是，当前的数据库不允许构建这样的系统，因为它们高度异构并且偏向严重的情况。本文分为三个部分：(i) 我们揭开了最新 COVID-19 分类模型实现的高灵敏度的神秘面纱，(ii) 在与西班牙格拉纳达圣塞西利奥临床大学医院的密切合作下，我们构建了 COVIDGR-1.0，一个同质且平衡的数据库，包括所有严重程度级别，从正常（RT-PCR 阳性）、轻度、中度到重度。COVIDGR-1.0 包含 426 个正向和 426 个负向 PA（PosteroAnterior）CXR 视图，并且 (iii) 我们提出了基于 COVID 智能数据的网络 (COVID-SDNet) 方法来提高 COVID 分类模型的泛化能力。我们的方法取得了良好且稳定的结果，准确度为$\text{97.72}\% \pm \text{0.95}\%$ ,$\text{86.90}\% \pm \text{3.20}\%$ ,$\text{61.80}\% \pm \text{5.49}\%$严重、中度和轻度 COVID-19 严重程度。我们的方法有助于及早发现 COVID-19。科学界可通过此链接获取 COVIDGR-1.0 以及严重级别标签https://dasci.es/es/transferencia/open-data/covidgr/ 。