ABSTRACT
COVID-19 has severe effects on several body organs, especially the lung. These severe effects result in features in the COVID-19 patients' Computed Tomography (CT) images distinct from other viral pneumonia. Although the primary diagnosis of COVID-19 is not primarily screened by CT, machine learning-based diagnosis systems early detect the COVID-19 lung abnormalities. Feature extraction is crucial for the success of traditional machine learning algorithms. Traditional machine learning algorithms utilize hand-crafted features to identify and classify patterns in an image. This paper utilizes the Gabor filters as the primary feature extractor for automated COVID-19 classification from lung CT images. We use a publicly available COVID-19 data-set of chest CT images to validate the performance and accuracy of the proposed model. The Gabor filter and other feature extractors with Random Forest classifiers achieved over 81% classification accuracy, the sensitivity of 81%, Specificity of 82%, and F1 score of 81%. © 2021 IEEE.