Novel coronavirus disease (nCOVID-19) is the most challenging problem for the world. The disease is caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2), leading to high morbidity and mortality worldwide. The study reveals that infected patients exhibit distinct radiographic visual characteristics along with fever, dry cough, fatigue, dyspnea, etc. Chest X-Ray (CXR) is one of the important, non-invasive clinical adjuncts that play an essential role in the detection of such visual responses associated with SARS-COV-2 infection. However, the limited availability of expert radiologists to interpret the CXR images and subtle appearance of disease radiographic responses remains the biggest bottlenecks in manual diagnosis. In this study, we present an automatic COVID screening (ACoS) system that uses radiomic texture descriptors extracted from CXR images to identify the normal, suspected, and nCOVID-19 infected patients. The proposed system uses two-phase classification approach (normal vs. abnormal and nCOVID-19 vs. pneumonia) using majority vote based classifier ensemble of five benchmark supervised classification algorithms. The training-testing and validation of the ACoS system are performed using 2088 (696 normal, 696 pneumonia and 696 nCOVID-19) and 258 (86 images of each category) CXR images, respectively. The obtained validation results for phase-I (accuracy (ACC) = 98.062%, area under curve (AUC) = 0.956) and phase-II (ACC = 91.329% and AUC = 0.831) show the promising performance of the proposed system. Further, the Friedman post-hoc multiple comparisons and z-test statistics reveals that the results of ACoS system are statistically significant. Finally, the obtained performance is compared with the existing state-of-the-art methods.

新型冠状病毒病（nCOVID-19）是世界上最具挑战性的问题。该疾病由严重急性呼吸综合征冠状病毒-2 (SARS-COV-2) 引起，导致全球发病率和死亡率较高。研究表明，感染患者表现出明显的放射学视觉特征，并伴有发烧、干咳、疲劳、呼吸困难等。胸部 X 光检查 (CXR) 是重要的非侵入性临床辅助手段之一，在检测中发挥着重要作用与 SARS-COV-2 感染相关的此类视觉反应。然而，放射科医生解释 CXR 图像和疾病放射学反应的微妙表现的能力有限，仍然是手动诊断的最大瓶颈。在这项研究中，我们提出了一种自动 COVID 筛查 (ACoS) 系统，该系统使用从 CXR 图像中提取的放射组学纹理描述符来识别正常、疑似和 nCOVID-19 感染患者。所提出的系统采用两阶段分类方法（正常与异常以及 nCOVID-19 与肺炎），使用五种基准监督分类算法的基于多数投票的分类器集合。 ACoS 系统的训练测试和验证分别使用 2088 张（696 张正常图像、696 张肺炎图像和 696 张 nCOVID-19）和 258 张（每个类别 86 张图像）CXR 图像进行。获得的 I 期验证结果（准确度 (ACC) = 98.062%，曲线下面积 (AUC) = 0.956）和 II 期验证结果（ACC = 91.329% 和 AUC = 0.831）显示了所提出系统的良好性能。此外，Friedman 事后多重比较和 z 检验统计表明 ACoS 系统的结果具有统计显着性。最后，将获得的性能与现有的最先进方法进行比较。