COVID-19 is a pandemic disease, which massively affected human lives in more than 200 countries. Caused by the coronavirus SARS-CoV-2, this acute respiratory illness affects the human lungs and can easily spread from person to person. Since the disease heavily affects human lungs, analyzing the X-ray images of the lungs may prove to be a powerful tool for disease investigation. In this research, we use the information contained within the complex structures of X-ray images between the cases of COVID-19 and other respiratory diseases, whereas the case of healthy lungs is taken as the reference point. To analyze X-ray images, we benefit from the concept of Shannon’s entropy and fractal theory. Shannon’s entropy is directly related to the amount of information contained within the X-ray images in question, whereas fractal theory is used to analyze the complexity of these images. The results, obtained in this study, show that the method of fractal analysis can detect the level of infection among different respiratory diseases and that COVID-19 has the worst effect on the human lungs. In other words, the complexity of X-ray images is proportional to the severity of the respiratory disease. The method of analysis, employed in this study, can be used even further to analyze how COVID-19 progresses in affected patients.

中文翻译：

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基于复杂性的冠状病毒病分类 (COVID-19)

COVID-19 是一种流行病，严重影响了 200 多个国家的人类生活。这种由冠状病毒 SARS-CoV-2 引起的急性呼吸道疾病会影响人的肺部，并且很容易在人与人之间传播。由于该疾病严重影响人类肺部，因此分析肺部的 X 射线图像可能被证明是疾病调查的有力工具。在这项研究中，我们使用了包含在 COVID-19 和其他呼吸道疾病病例之间的复杂 X 射线图像结构中的信息，而以健康肺部的病例为参考点。为了分析 X 射线图像，我们受益于香农熵和分形理论的概念。香农的熵与相关 X 射线图像中包含的信息量直接相关，而分形理论用于分析这些图像的复杂性。本研究获得的结果表明，分形分析方法可以检测不同呼吸系统疾病之间的感染水平，并且 COVID-19 对人体肺部的影响最严重。换言之，X 射线图像的复杂性与呼吸系统疾病的严重程度成正比。本研究中采用的分析方法可用于进一步分析 COVID-19 在受影响患者中的进展情况。X 射线图像的复杂性与呼吸系统疾病的严重程度成正比。本研究中采用的分析方法可用于进一步分析 COVID-19 在受影响患者中的进展情况。X 射线图像的复杂性与呼吸系统疾病的严重程度成正比。本研究中采用的分析方法可用于进一步分析 COVID-19 在受影响患者中的进展情况。