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

arXiv: https://arxiv.org/abs/2108.05536 · HTML: https://mlsanity.com/paper/2108.05536/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography](https://mlsanity.com/paper/1807.03058/) (2018)

## Abstract (arXiv)

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.