Classification of Chest X-Ray Images using Wavelet and MFCC Features and Support Vector Machine Classifier


  • H. A. Owida Medical Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Jordan
  • A. Al-Ghraibah Medical Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Jordan
  • M. Altayeb Electronics and Communications Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Jordan
Volume: 11 | Issue: 4 | Pages: 7296-7301 | August 2021 |


The shortage and availability limitation of RT-PCR test kits and is a major concern regarding the COVID-19 pandemic. The authorities' intention is to establish steps to control the propagation of the pandemic. However, COVID-19 is radiologically diagnosable using x-ray lung images. Deep learning methods have achieved cutting-edge performance in medical diagnosis software assistance. In this work, a new diagnostic method for detecting COVID-19 disease is implemented using advanced deep learning. Effective features were extracted using wavelet analysis and Mel Frequency Cepstral Coefficients (MFCC) method, and they used in the classification process using the Support Vector Machine (SVM) classifier. A total of 2400 X-ray images, 1200 of them classified as Normal (healthy) and 1200 as COVID-19, have been derived from a combination of public data sets to verify the validity of the proposed model. The experimental results obtained an overall accuracy of 98.8% by using five wavelet features, where the classification using MFCC features, MFCC-delta, and MFCC-delta-delta features reached accuracy around 97% on average. The results show that the proposed model has reached the required level of success to be applicable in COVID 19 diagnosis.


chest X-ray images, classification of chest diseases, covid-19, feature extraction, Support Vector Machine (SVM)


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How to Cite

H. A. Owida, A. Al-Ghraibah, and M. Altayeb, “Classification of Chest X-Ray Images using Wavelet and MFCC Features and Support Vector Machine Classifier”, Eng. Technol. Appl. Sci. Res., vol. 11, no. 4, pp. 7296–7301, Aug. 2021.


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