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Lung X-ray images, if processed using statistical and computational methods,can distinguish pneumonia from COVID-19. The present work shows that it ispossible to extract lung X-ray characteristics to improve the methods ofexamining and diagnosing patients with suspected COVID-19, distinguishing themfrom malaria, dengue, H1N1, tuberculosis, and Streptococcus pneumonia. Moreprecisely, an intelligent computational model was developed to process lungX-ray images and classify whether the image is of a patient with COVID-19. Theimages were processed and extracted their characteristics. Thesecharacteristics were the input data for an unsupervised statistical learningmethod, PCA, and clustering, which identified specific attributes of X-rayimages with Covid-19. The introduction of statistical models allowed a fastalgorithm, which used the X-means clustering method associated with theBayesian Information Criterion (CIB). The developed algorithm efficientlydistinguished each pulmonary pathology from X-ray images. The method exhibitedexcellent sensitivity. The average recognition accuracy of COVID-19 was 0.93and 0.051.

Intelligent computational model for the classification of Covid-19 with chest radiography compared to other respiratory diseases