COVID-19 Identification using Machine Learning Classifiers with GLCM Features of Chest X-ray Images

Abstract

COVID-19 is an ongoing pandemic, and is also known by the name coronavirus. It was originally discovered in Wuhan, China, in December, 2019. Since then, it has been increasing rapidly worldwide. Since it has been increasing at such a rapid pace, testing equipment has limited availability. Also, this disease spreads very quickly, so it is better if it is detected earlier, in order so that it can be stopped from spreading. Therefore, the importance of early detection has increased; however, because of the shortage of testing sets, it is a necessity to develop an automated system that can detect whether the COVID-19 disease is present in a person or not as early as possible. Therefore, in this work, to extract features from X-ray images of the chest, we have made use of the Gray Level Co-occurrence Matrix (GLCM). After extracting these features for the classification of the images, we used different machine learning models, and an ensemble of machine learning models, to classify X-ray images of the chest as COVID-19, Normal, Pneumonia-bac, or Pneumonia-vir. Considering the average of performance metrics, the ensemble of Random Forest-MLP gave the best result among the variations.