论文标题
COVIDGR数据集和COVID-SDNET方法，用于基于胸部X射线图像预测COVID-19
COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images
论文作者
论文摘要
目前，使用RT-PCR测试，CT扫描和/或胸部X射线（CXR）图像诊断出冠状病毒病（Covid-19）是21世纪最感染性疾病之一。 CT（计算机断层扫描）扫描仪和RT-PCR测试在大多数医疗中心都不可用，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时间/成本效益工具。深度学习神经网络具有建立COVID-19分类系统并检测COVID-19患者，尤其是严重程度低的患者的巨大潜力。不幸的是，当前的数据库不允许构建此类系统，因为它们是高度异质的，并且偏向严重的情况。本文是三个方面的：（i）我们揭示了最近的Covid-19分类模型所获得的高敏感性，（ii）在西班牙格拉纳达的医院克里尼科尼奥·圣塞西里奥（ClínicoSan Cecilio）密切合作，我们建立了Covidgr-1.0，我们建立了一个同质和平衡的数据库，其中包括正常级别的高度级别的均等级别，包括一个同质和平衡的数据库。 COVIDGR-1.0包含426个正和426个负PA（后）CXR视图和（iii），我们提出了基于COVID智能数据的网络（COVID-SDNET）方法，用于提高共同分类模型的泛化能力。我们的方法取得了良好和稳定的效果，准确度为97.72美元\％\ pm 0.95 \％$，$ 86.90 \％\％\ pm 3.20 \％$，$ 61.80 \％\％\％\％\ pm 5.49 \％$ $ $ $ 5.49 \％$严重，中度和轻度19性的严重性水平接受生物学，并适用于生物学的杂志，并适用于杂志。我们的方法可以帮助早期发现Covid-19。 Covidgr-1.0以及严重程度的标签可通过此链接https://dasci.es/es/trans/transferencia/open-data/covidgr/使用。
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 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 $97.72\% \pm 0.95 \%$, $86.90\% \pm 3.20\%$, $61.80\% \pm 5.49\%$ 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/.
