Improved Classification of Coronavirus Disease (COVID-19) based on Combination of Texture Features using CT Scan and X-ray Images
Luqy Nailur Rohmah, Alhadi Bustamam
Abstract
The novel coronavirus (also known as COVID19) has infected more than 20 million people worldwide and has now become a global pandemic. It is necessary to perform initial screenings to control the spread of the disease. Computed Tomography (CT) scan and X-ray images play an essential role in diagnosing the lung condition of patients with COVID-19 symptoms. Therefore, a machine learning method is needed to help in the early detection of COVID-19 patients through CT scan and X-ray images. In this research, we propose a machine learning model that can classify COVID-19 based on texture features techniques. In particular, there are three texture features, namely Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Histogram of Oriented Pattern (HOG), chosen as the feature extractors. To improve classification accuracy and computational efficiency, we combined these features with principal component analysis as a feature reduction. We evaluated each feature set individually and in groups. For the final step, we conducted a classification process using Support Vector Machine (SVM) algorithm. The proposed method's performance was implemented on a publicly available COVID-19 dataset that includes 1100 CT scans and 1100 X-ray images. The results show that combining GLCM, LBP, and HOG features can provide accuracy up to 97% on CT images and 99% accuracy on X-ray images.