A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In the proposed approach, the textural features are extracted from the chest X-ray images and local binary pattern (LBP) based images. Further, the image-based and LBP image-based features are jointly investigated. Thereafter, highly discriminatory features are provided to the classifier for developing an automated model for COVID-19 identification. The performance of the proposed approach is investigated over 2905 chest X-ray images of normal, pneumonia, and COVID-19 infected persons on various class combinations to analyze the robustness. The developed method achieves 97.97% accuracy (acc) and 99.88% sensitivity (sen) for classifying COVID-19 X-ray images against pneumonia infected and normal person's X-ray images. It attains 98.91% acc and 99.33% sen for COVID-19 X-ray against the normal X-ray classification. This method can be employed to assist the radiologists during mass screening for fast, accurate, and contact-free COVID-19 diagnosis.

实时 COVID-19 检测系统是当前形势的最高要求。本文介绍了一种基于胸部 X 射线图像的自动 COVID-19 检测系统，该系统可与 RT-PCR 测试一起使用，以提高诊断率。在所提出的方法中，纹理特征是从胸部 X 射线图像和基于局部二进制模式 (LBP) 的图像中提取的。此外，联合研究了基于图像和基于 LBP 图像的特征。此后，向分类器提供高度区分的特征，用于开发用于 COVID-19 识别的自动化模型。该方法的性能在 2905 幅不同类别组合的正常人、肺炎和 COVID-19 感染者的胸部 X 光图像上进行了调查，以分析其稳健性。开发的方法达到了 97.97% 的准确率 (acc) 和 99。将 COVID-19 X 射线图像与肺炎感染者和正常人的 X 射线图像进行分类的灵敏度 (sen) 为 88%。COVID-19 X 射线相对于正常 X 射线分类达到 98.91% acc 和 99.33% sen。这种方法可用于在大规模筛查期间协助放射科医生进行快速、准确和无接触的 COVID-19 诊断。