Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, dengue, H1N1, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. The images were processed and extracted their characteristics. These characteristics were the input data for an unsupervised statistical learning method, PCA, and clustering, which identified specific attributes of X-ray images with Covid-19. The introduction of statistical models allowed a fast algorithm, which used the X-means clustering method associated with the Bayesian Information Criterion (CIB). The developed algorithm efficiently distinguished each pulmonary pathology from X-ray images. The method exhibited excellent sensitivity. The average recognition accuracy of COVID-19 was 0.93 and 0.051.
翻译:通过统计和计算方法处理肺部X射线图像,可以区分肺炎和COVID-19。目前的工作表明,可以提取肺部X射线特征,以改进对疑似COVID-19的病人进行检查和诊断的方法,将他们与疟疾、登革热、H1N1、肺结核和Streptococcccccus肺炎区分开来。更准确地说,开发了一个智能计算模型,处理肺部X射线图像,并区分该图像是否为患有COVID-19的病人。这些图像经过处理并提取了它们的特征。这些特征是用于一种未经监督的统计学习方法、五氯苯甲醚和聚群的输入数据,其中确定了与Covid-19的X射线图像的具体属性。采用统计模型可以快速算法,使用与Bayesian信息标准相关的X手段组方法。开发的算法有效地区分了每一种肺部病理学和X光图像。该方法具有很好的敏感性。COVID-19的平均识别精确度为0.93和0.051。