COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images.

Autor:	Hasoon JN; Department of Computer Science, Mustansiriyah University, 10001 Baghdad, Iraq., Fadel AH; Department of Computer Science, University of Diyala, 32001 Diyala, Iraq., Hameed RS; Department of Computer Science, University of Diyala, 32001 Diyala, Iraq., Mostafa SA; Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Johor, Malaysia., Khalaf BA; Department of Medical Instruments Engineering Techniques, Bilad Alrafidain University College, 32001 Diyala, Iraq., Mohammed MA; College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq., Nedoma J; Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic.
Jazyk:	angličtina
Zdroj:	Results in physics [Results Phys] 2021 Dec; Vol. 31, pp. 105045. Date of Electronic Publication: 2021 Nov 22.
DOI:	10.1016/j.rinp.2021.105045
Abstrakt:	The term COVID-19 is an abbreviation of Coronavirus 2019, which is considered a global pandemic that threatens the lives of millions of people. Early detection of the disease offers ample opportunity of recovery and prevention of spreading. This paper proposes a method for classification and early detection of COVID-19 through image processing using X-ray images. A set of procedures are applied, including preprocessing (image noise removal, image thresholding, and morphological operation), Region of Interest (ROI) detection and segmentation, feature extraction, (Local binary pattern (LBP), Histogram of Gradient (HOG), and Haralick texture features) and classification (K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)). The combinations of the feature extraction operators and classifiers results in six models, namely LBP-KNN, HOG-KNN, Haralick-KNN, LBP-SVM, HOG-SVM, and Haralick-SVM. The six models are tested based on test samples of 5,000 images with the percentage of training of 5-folds cross-validation. The evaluation results show high diagnosis accuracy from 89.2% up to 98.66%. The LBP-KNN model outperforms the other models in which it achieves an average accuracy of 98.66%, a sensitivity of 97.76%, specificity of 100%, and precision of 100%. The proposed method for early detection and classification of COVID-19 through image processing using X-ray images is proven to be usable in which it provides an end-to-end structure without the need for manual feature extraction and manual selection methods. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2021 The Author(s).)
Databáze:	MEDLINE
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