Intelligent computational model for the classification of Covid-19 with chest radiography compared to other respiratory diseases
This work addresses the need for efficient diagnostic tools for COVID-19 detection in medical imaging, though it appears incremental as it applies existing methods like PCA and X-means clustering to a new dataset.
The researchers tackled the problem of distinguishing COVID-19 from other respiratory diseases like malaria and pneumonia using chest X-rays, achieving an average recognition accuracy of 0.93.
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.