Coronavirus disease (COVID-19) has been the main agenda of the whole world ever since it came into sight. X-ray imaging is a common and easily accessible tool that has great potential for COVID-19 diagnosis and prognosis. Deep learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large data sets. However, data scarcity can be a crucial obstacle when using them for COVID-19 detection. Alternative approaches such as representation-based classification [collaborative or sparse representation (SR)] might provide satisfactory performance with limited size data sets, but they generally fall short in performance or speed compared to the neural network (NN)-based methods. To address this deficiency, convolution support estimation network (CSEN) has recently been proposed as a bridge between representation-based and NN approaches by providing a noniterative real-time mapping from query sample to ideally SR coefficient support, which is critical information for class decision in representation-based techniques. The main premises of this study can be summarized as follows: 1) A benchmark X-ray data set, namely QaTa-Cov19, containing over 6200 X-ray images is created. The data set covering 462 X-ray images from COVID-19 patients along with three other classes; bacterial pneumonia, viral pneumonia, and normal. 2) The proposed CSEN-based classification scheme equipped with feature extraction from state-of-the-art deep NN solution for X-ray images, CheXNet, achieves over 98% sensitivity and over 95% specificity for COVID-19 recognition directly from raw X-ray images when the average performance of 5-fold cross validation over QaTa-Cov19 data set is calculated. 3) Having such an elegant COVID-19 assistive diagnosis performance, this study further provides evidence that COVID-19 induces a unique pattern in X-rays that can be discriminated with high accuracy.

中文翻译：

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X 射线图像中基于卷积稀疏支持估计器的 COVID-19 识别。

冠状病毒病（COVID-19）自出现以来一直是全世界的主要议程。X 射线成像是一种常见且易于使用的工具，在 COVID-19 诊断和预后方面具有巨大潜力。当在大型数据集上进行适当训练时，深度学习技术通常可以在许多分类任务中提供最先进的性能。但是，在将它们用于 COVID-19 检测时，数据稀缺可能是一个关键障碍。替代方法，例如基于表示的分类 [协作或稀疏表示 (SR)] 可能会在有限大小的数据集下提供令人满意的性能，但与基于神经网络 (NN) 的方法相比，它们通常在性能或速度上有所不足。为了解决这个不足，卷积支持估计网络 (CSEN) 最近被提出作为基于表示和 NN 方法之间的桥梁，通过提供从查询样本到理想 SR 系数支持的非迭代实时映射，这是基于表示的技术中类别决策的关键信息. 本研究的主要前提可以概括如下：1）创建了一个基准 X 射线数据集，即 QaTa-Cov19，其中包含 6200 多张 X 射线图像。该数据集涵盖来自 COVID-19 患者和其他三个类别的 462 张 X 射线图像；细菌性肺炎、病毒性肺炎和正常。2) 所提出的基于 CSEN 的分类方案配备了从最先进的 X 射线图像深度神经网络解决方案 CheXNet 中提取特征，当计算对 QaTa-Cov19 数据集的 5 倍交叉验证的平均性能时，直接从原始 X 射线图像识别 COVID-19 的灵敏度和特异性超过 95%。3) 具有如此优雅的 COVID-19 辅助诊断性能，本研究进一步提供了证据表明 COVID-19 在 X 射线中诱导出一种独特的模式，可以高精度地进行区分。