Screening of Viral Pneumonia and COVID-19 in Chest X-ray using Classical Machine Learning

Abstract

Governments, civil society, health professionals, and scientists have been facing a relentless fight against the pandemic of the COVID-19 disease; however, there are already about 150 million people infected worldwide and more than 3 million lives claimed, and numbers keep rising. One of the ways to combat this disease is the effective screening of infected patients. However, COVID-19 provides a similar pattern with diseases, such as pneumonia, and can misguide even very well-trained physicians. In this sense, a chest X-ray (CXR) is an effective alternative due to its low cost, accessibility, and quick response. Thus, inspired by research on the use of CXR for the diagnosis of COVID-19 pneumonia, we investigate classical machine learning methods to assist in this task. The main goal of this work is to present a robust, lightweight, and fast technique for the automatic detection of COVID-19 from CXR images. We extracted radiomic features from CXR images and trained classical machine learning models for two different classification schemes: i) COVID-19 pneumonia vs. Normal ii) COVID-19 vs. Normal vs. Viral pneumonia. Several evaluation metrics were used and comparison with many studies is presented. Our experimental results are equivalent to the state-of-the-art for both classification schemes. The solution’s high performance makes it a viable option as a computer-aided diagnostic tool, which can represent a significant gain in the speed and accuracy of the COVID-19 diagnosis.

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