Estimación del Grado de Afectación Pulmonar por COVID-19 Mediante la Clasificación Supervisada de la Imagen de Rayos X

Eduardo  Garea Llano; Hector Adrian Castellanos Loaces; Eduardo Martínez Montes; Evelio González Dalmau

doi:10.5281/zenodo.5545908

Authors

Eduardo Garea Llano Centro de Neurociencias de Cuba
Hector Adrian Castellanos Loaces Centro de Neurociencias de Cuba
Eduardo Martínez Montes Centro de Neurociencias de Cuba
Evelio González Dalmau Centro de Neurociencias de Cuba

DOI:

https://doi.org/10.5281/zenodo.5545908

Keywords:

Machine learning; Supervised classification; COVID-19; x-ray images; Digital image processing

Abstract

This work presents the proposal of a simple quantitative index of lung affection derived from chest x-ray images in patients diagnosed with COVID-19 in an advanced stage of the disease. The index is obtained from an algorithm that combines digital image processing and machine learning methods for the segmentation of the lung region, the evaluation of its quality and the classification of each pixel of the segmented lung image.

The results achieved in the experiments carried out on images of healthy patients and those affected by COVID-19 showed high values of sensitivity and specificity in the classification. The study of the variation of the values of the proposed index in time series of images from patients with COVID 19 admitted to the intensive care units of hospitals in Havana, Cuba, showed a relationship between these variations with the evolution of the disease and the patients’ response the proposed index as an indicator of the effectiveness of the treatments and protocols applied.

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