Coronavirus (Covid-19) Classification Using CT Images by Machine Learning Methods

By Mücahid Barstuğan, Umut Ozkaya, Ş. Öztürk. 2020

348 citations recorded by the source corpus.

This study presents early phase detection of Coronavirus (COVID-19), which is named by World Health Organization (WHO), by machine learning methods. The detection process was implemented on abdominal Computed Tomography (CT) images. The expert radiologists detected from CT images that COVID-19 shows different behaviours from other viral pneumonia. Therefore, the clinical experts specify that COVID-19 virus needs to be diagnosed in early phase. For detection of the COVID-19, four different datasets were formed by taking patches sized as 16x16, 32x32, 48x48, 64x64 from 150 CT images. The feature extraction process was applied to patches to increase the classification performance. Grey Level Co-occurrence Matrix (GLCM), Local Directional Pattern (LDP), Grey Level Run Length Matrix (GLRLM), Grey-Level Size Zone Matrix (GLSZM), and Discrete Wavelet Transform (DWT) algorithms were used as feature extraction methods. Support Vector Machines (SVM) classified the extracted features. 2-fold, 5-fold and 10-fold cross-validations were implemented during the classification process. Sensitivity, specificity, accuracy, precision, and F-score metrics were used to evaluate the classification performance. The best classification accuracy was obtained as 99.68% with 10-fold cross-validation and GLSZM feature extraction method.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects (2020, background): “, in contact tracing [43], [44], symptom screening [45], risk scoring [46], vaccine development [47], or diagnosis from CT [48] or Xray [49] imaging—see, e.”
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, methodology): “During the classification process, 2-fold, 5-fold, and 10-fold cross-validation methods were used [29].”
Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach (2020, methodology): “[16] proposed a machine learning approach for COVID-19 classification from CT images.”
Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning (2020, methodology): “Barstugan et al. (2020) utilized four different feature extraction techniques to extract features from chest CT images.”
Leveraging Data Science to Combat COVID-19: A Comprehensive Review (2020, methodology): “[135] Chest CT scans Conventional Feature Extraction techniques + SVM GLCM, LDP, GLRLM, GLSZM, and DWT algorithms are used as feature extraction and SVM for classification.”
Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts (2020, methodology): “The use of ML and DL techniques with chest CT scans for COVID-19 detection were considered in [42], [43], [55]–[58], respectively.”