The novel coronavirus (SARS-CoV-2) is spreading rapidly worldwide, and it has become a greater risk for human beings. To curb the community transmission of this virus, rapid detection and identification of the affected people via a quick diagnostic process are necessary. Media studies have shown that most COVID-19 victims endure lung disease. For rapid identification of the affected patient, chest CT scans and X-ray images have been reported to be suitable techniques. However, chest X-ray (CXR) shows more convenience than the CT imaging techniques because it has faster imaging times than CT and is also simple and cost-effective. Literature shows that transfer learning is one of the most successful techniques to analyze chest X-ray images and correctly identify various types of pneumonia. Since SVM has a remarkable aspect that tremendously provides good results using a small data set thus in this study we have used SVM machine learning algorithm to diagnose COVID-19 from chest X-ray images. The image processing tool called RGB and SqueezeNet models were used to get more images to diagnose the available data set. Our adopted model shows an accuracy of 98.8% to detect the COVID-19 affected patient from CXR images. It is expected that our proposed computer-aided detection tool (CAT) will play a key role in reducing the spread of infectious diseases in society through a faster patient screening process.

中文翻译：

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开发计算机辅助工具，使用机器学习算法从 CXR 图像中检测 COVID-19 肺炎

新型冠状病毒（SARS-CoV-2）正在全球范围内迅速传播，已成为人类面临的更大风险。为了遏制这种病毒的社区传播，有必要通过快速诊断过程快速检测和识别受影响的人。媒体研究表明，大多数 COVID-19 受害者患有肺部疾病。据报道，为了快速识别受影响的患者，胸部 CT 扫描和 X 射线图像是合适的技术。然而，胸部 X 射线 (CXR) 比 CT 成像技术更方便，因为它的成像时间比 CT 更快，而且简单且具有成本效益。文献表明，迁移学习是分析胸部 X 射线图像并正确识别各种类型肺炎的最成功的技术之一。由于 SVM 具有一个显着的特点，即使用小数据集即可提供良好的结果，因此在本研究中，我们使用 SVM 机器学习算法从胸部 X 射线图像诊断 COVID-19。使用称为 RGB 和 SqueezeNet 模型的图像处理工具来获取更多图像来诊断可用数据集。我们采用的模型从 CXR 图像中检测出受 COVID-19 影响的患者的准确度为 98.8%。预计我们提出的计算机辅助检测工具（CAT）将通过更快的患者筛查过程在减少传染病在社会中的传播方面发挥关键作用。