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This study presents early phase detection of Coronavirus (COVID-19), which isnamed by World Health Organization (WHO), by machine learning methods. Thedetection process was implemented on abdominal Computed Tomography (CT) images.The expert radiologists detected from CT images that COVID-19 shows differentbehaviours from other viral pneumonia. Therefore, the clinical experts specifythat COV\.ID-19 virus needs to be diagnosed in early phase. For detection ofthe COVID-19, four different datasets were formed by taking patches sized as16x16, 32x32, 48x48, 64x64 from 150 CT images. The feature extraction processwas applied to patches to increase the classification performance. Grey LevelCo-occurrence Matrix (GLCM), Local Directional Pattern (LDP), Grey Level RunLength Matrix (GLRLM), Grey-Level Size Zone Matrix (GLSZM), and DiscreteWavelet Transform (DWT) algorithms were used as feature extraction methods.Support Vector Machines (SVM) classified the extracted features. 2-fold, 5-foldand 10-fold cross-validations were implemented during the classificationprocess. Sensitivity, specificity, accuracy, precision, and F-score metricswere used to evaluate the classification performance. The best classificationaccuracy was obtained as 99.68% with 10-fold cross-validation and GLSZM featureextraction method.

Coronavirus (COVID-19) Classification using CT Images by Machine Learning Methods