Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification

  • Hiam Alquran Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, 21163, Jordan
  • Mohammad Alsleti The Institute of Biomedical Technology, King Hussein Medical Center, Royal Jordanian Medical Service, Amman, 11855, Jordan
  • Roaa Alsharif College of applied medical Sciences, Radiological Science Program, King Saud University, Jeddah, 21435, Saudi Arabia
  • Isam Abu Qasmieh Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, 21163, Jordan
  • Ali Mohammad Alqudah Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, 21163, Jordan
  • Nor Hazlyna Binti Harun Data Science Research Lab, School of Computing, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia
Keywords: COVID-19, COVID-19 pandemic, Respiratory infection detection, Pneumonia, K-Nearest Neighbor, Support Vector Machine, Random Forest


The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient; as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival.


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How to Cite
Alquran, H., Alsleti, M., Alsharif, R., Abu Qasmieh, I., Alqudah, A. and Binti Harun, N.H. 2021. Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification. MENDEL. 27, 1 (Jun. 2021), 9-17. DOI:https://doi.org/10.13164/mendel.2021.1.009.
Research articles