Barstuğan, MücahidÖzkaya, U.Öztürk, Ş.2021-12-132021-12-1320211613-0073https://hdl.handle.net/20.500.13091/2254th International Conference on Recent Trends and Applications in Computer Science and Information Technology, RTA-CSIT 2021 -- 21 May 2021 through 22 May 2021 -- -- 169296This study detected the Coronavirus (COVID-19) disease by implementing artificial learning methods. Coronavirus disease occurs in the lungs and can cause death. The detection process was performed on chest Computed Tomography (CT) images. The training process was implemented by using 32x32 patches that were obtained from CT images. This study includes three phases: The first phase classifies patches by the SVM algorithm without implementing the feature extraction methods. The second phase extracts features on patches by using Grey Level Co-occurrence Matrix (GLCM), Grey Level Run Length Matrix (GLRLM), Grey-Level Size Zone Matrix (GLSZM), Discrete Wavelet Transform (DWT), Fast Fourier Transform (FFT), and Discrete Cosine Transform (DCT) methods and classifies the features extracted. The third phase uses Convolutional Neural Networks (CNN) method to classify the patches. 10-fold cross-validation is implemented in the classification process. The sensitivity, specificity, accuracy, precision, and F-score metrics measure the classification performance. The highest classification accuracy was achieved as 99.15% by the CNN method during the training process. The classification structure, which has the highest classification accuracy, was used during the test performance and had 80.21% mean sensitivity rate, which is the COVID detection performance, on 727 test images. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).eninfo:eu-repo/semantics/closedAccessClassificationCoronavirusCOVID-19CT imagesDeep LearningFeature extractionMachine learningConference Object2-s2.0-85107831990