Screening of Viral Pneumonia and COVID 19 in Chest X ray using Classical Machine Learning
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.
2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)