Implementation of Machine Learning Techniques for the Classification of Lung X-Ray Images Used to Detect COVID-19 in Humans

  • Maad M. Mijwil Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Baghdad, Iraq
Keywords: Machine Learning, X-Ray Images, COVID-19, Classification, Artificial Intelligence

Abstract

COVID-19 (Coronavirus disease-2019), commonly called Coronavirus or CoV, is a dangerous disease caused by the SARS-CoV-2 virus. It is one of the most widespread zoonotic diseases around the world, which started from one of the wet markets in Wuhan city. Its symptoms are similar to those of the common flu, including cough, fever, muscle pain, shortness of breath, and fatigue. This article suggests implementing machine learning techniques (Random Forest, Logistic Regression, Naïve Bayes, Support Vector Machine) by Python to classify a series of chest X-ray images that include viral pneumonia, COVID-19, and healthy (Not infected) cases in humans. The study includes more than 1400 images that are collected from the Kaggle platform. The experimental outcomes of this study confirmed that the supported vector machine technique has high accuracy and excellent performance in the classification of the disease, as reflected by values of 91.8% accuracy, 91.7% sensitivity, 95.9% specificity, 91.8% F1-score, and 97.6% AUC.

Published
2021-07-01
How to Cite
Mijwil, M. M. (2021). Implementation of Machine Learning Techniques for the Classification of Lung X-Ray Images Used to Detect COVID-19 in Humans. Iraqi Journal of Science, 62(6), 2099-2109. https://doi.org/10.24996/ijs.2021.62.6.35
Section
Computer Science