Automatic Classification of Covid-19 Chest X-Ray Images Using Local Binary Pattern and Binary Particle Swarm Optimization for Feature Selection
Abstract
Novel Coronavirus disease 2019 (COVID-19) is a type of pandemic viruses that cause respiratory tract infection in humans. The clinical imaging of Chest X-Ray (CXR) by Computer Aided Diagnosis (CAD) plays an important role to identify the patients who infected by COVID-19. The objective of this paper presents a Computer Aided Diagnosis (CAD) method for automatically classify 110 frontal CXR images of contagious people according to Normal and COVID-19 infection. The proposed method contains of four phases: image enhancement, feature extraction, feature selection and classification. Gaussian filter is performed to de-noise the images and Adaptive Histogram Equalization (AHE) for image enhancement in pre-processing step for better decision-making process. Local Binary Pattern (LBP) features set are extracted from the dataset. Binary Particle Swarm Optimization (BPSO) is considered to select the clinically relevant features and developing the robust model. The successive features are fed to Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. The experimental results show that the system robustness in classification COVID-19 from Normal images with average accuracy 94.6%, sensitivity 96.2% and specificity 93%.
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M. Y. Ng, E. Y. P. Lee, J. Yang, F. Yang, X. Li, H. Wang, M. M. S. Lui, C. S. Y. Lo, B. Leung, P. L. Khong, C. K. M. Hui, K. Y. Yuen, and M. D. Kuo. Imaging profile of the COVID-19 infection: Radiologic findings and literature review. Radiology: Cardiothoracic Imaging, vol. 2, no. 1, p. e200034, 2020.
M. Thomas, G. Ksiazek, D, Erdman, C. S. Goldsmith, S. R. Zaki, T. Peret, S. Emery, S. Tong, and C. Urbani. Respiratory and Enteric Virus Branch. United States: National Center for Immunization and Respiratory Diseases, 2003.
M. Chan-Yeung, and R. Xu. SARS: Epidemiology. Respirology, vol. 8, pp. S9-S14, 2003.
A. Zumla, D. S. Hui, and S. Perlman. Middle East respiratory syndrome. The Lancet, vol. 386, no. 9997, pp. 995-1007, 2015.
A. J. Rodriguez-Morales, J. A. Cardona-Ospina, E. GutiérrezOcampo, R. Villamizar-Peña, Y. Holguin-Rivera, J. P. EscaleraAntezana, L. E. Alvarado-Arnez, D. K. Bonilla-Aldana, C. Franco-Paredes, A. F. Henao-Martinez, A. Paniz-Mondolfi, G. J. Lagos-Grisales, E. Ramírez-Vallejo, J. A. Suárez, L. I. Zambrano, W. E. Villamil-Gómez, G. J. Balbin-Ramon, A. A. Rabaan, H. Harapan, K. Dhama, H. Nishiura, H. Kataoka, T. Ahmad, and R. Sah. Clinical, laboratory and imaging features of COVID19: A systematic review and meta-analysis. Travel Medicine and Infectious Disease, vol. 34, p. 101623, 2020.
H. Y. F. Wong, H. Y. S. Lam, A. H. T. Fong, S. T. Leung, T. W. Y. Chin, C. S. Y. Lo, M. M. S. Lui, J. C. Y. Lee, K. W. H. Chiu, T. W. H. Chung, E. Y. P. Lee, E. Y. F. Wan, I. F. N. Hung, T. P. W. Lam, M. D. Kuo, and M. Y. Ng. Frequency and distribution of chest radiographic findings in COVID-19 positive patients. Radiology, vol. 296, No. 2, pp. 72-78, 2020.
L. Fan, D. Li, H. Xue, L. Zhang, Z. Liu, B. Zhang, L. Zhang, W. Yang, B. Xie, X. Duan, X. Hu, K. Cheng, L. Peng, N. Yu, L. Song, H. Chen, X. Sui, N. Zheng, S. Liu, and Z. Jin. Progress and prospect on imaging diagnosis of COVID-19. Chinese Journal of Academic Radiology, vol. 3, no. 1, pp. 4-13, 2020.
M. E. H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir, Z. B. Mahbub, K. R. Islam, M. S. Khan, A. Iqbal, N. Al-Emadi, M. B. I. Reaz, and T. I. Islam. Can AI help in screening viral and COVID-19 pneumonia? arXiv, vol. 8, pp. 132665–132676, 2020.
A. Abbas, M. M. Abdelsamea, and M. M. Gaber. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Applied Intelligence, vol. 51, no. 2, pp. 854-864, 2021.
K. Purohit, A. Kesarwani, D. R. Kisku, and M. Dalui. COVID-19 detection on chest X-Ray and CT Scan images using multi-image augmented deep learning model. BioRxiv. vol. 1, p. 205567, 2020.
A. Narin, C. Kaya, and Z. Pamuk. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Analysis and Applications, vol. 24, pp. 1207-1220, 2021.
K. El Asnaoui, and Y. Chawki. Using X-ray images and deep learning for automated detection of coronavirus disease. Journal of Biomolecular Structure and Dynamics, vol. 39, no. 10, pp. 3615-3626, 2020.
S. H. Wady, R. Z. Yousif, and H. R. Hasan. A novel intelligent system for brain tumor diagnosis based on a composite neutrosophic-slantlet transform domain for statistical texture feature extraction. BioMed Research International, vol. 2020, p. 8125392, 2020.
B. N. S. Mohammed, and R. Z. Yousif. Intelligent system for screening diabetic retinopathy by using neutrosophic and statistical fundus image features. Zanco Journal of Pure and Applied Sciences, vol. 31, no. 6, pp. 30-39, 2019.
A. Malhotra, A. Sankaran, A. Mittal, M. Vatsa, and R. Singh. Fingerphoto authentication using smartphone camera captured under varying environmental conditions. In: Human Recognition in Unconstrained Environments: Using Computer Vision, Pattern Recognition and Machine Learning Methods for Biometrics. Netherlands: Elsevier, pp. 119-144, 2017.
Y. Cai, G. Xu, A. Li, and X. Wang. A novel improved local binary pattern and its application to the fault diagnosis of diesel engine. Shock and Vibration, vol. 2020, p. 9830162, 2020.
M. L. Bermingham, R. Pong-Wong, A. Spiliopoulou, C. Hayward, I. Rudan, H. Campbell, A. F. Wright, J. F. Wilson, F. Agakov, P. Navarro, and C. S. Haley. Application of high-dimensional feature selection: Evaluation for genomic prediction in man. Scientific Reports, vol. 5, p. 151, 2015.
S. W. Lin, K. C. Ying, S. C. Chen, and Z. J. Lee. Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications, vol. 35, no. 4, pp. 1817-1824, 2008.
J. Too, A. R. Abdullah, and N. M. Saad. A new co-evolution binary particle swarm optimization with multiple inertia weight strategy for feature selection. Informatics, vol. 6, no. 2, p. 21, 2019.
V. Kothari, J. Anuradha, S. Shah, and P. Mittal. A survey on particle swarm optimization in feature selection. In Communications in Computer and Information Science, vol. 270, pp. 192-201, 2012.
L. R. Fleah and S. A. Al-Aubi. A face recognition system based on principal component analysis-wavelet and support vector machines. Cihan University-Erbil Scientific Journal, vol. 3, no. 2, pp. 14-20, 2019.
R. Nisbet, G. Miner, and K. Yale. Handbook of Statistical Analysis and Data Mining Applications. Cambridge, Massachusetts: Academic Press, 2017.
S. Jang, Y. E. Jang, Y. J. Kim, and H. Yu. Input initialization for inversion of neural networks using k-nearest neighbor approach. Information Science, vol. 519, pp. 229-242, 2020.
E. El-Din Hemdan, M. A. Shouman, and M. E. Karar. COVIDX-net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images. arXiv:2003.11055v1, 2020.
D. R. J. Ramteke, and K. Y. Monali. Automatic medical image classification and abnormality detection using K-nearest neighbour. International Journal of Advanced Computer Research, vol. 2, no 4, p. 6, 2012.
S. Baity. COVID-19 detection for chest X-ray images using local binary pattern. International Journal of Emerging Trends in Engineering Research, vol. 8, pp. 78-81, 2020
Copyright (c) 2021 Bazhdar N. Mohammed, Firas H. Al-Mukhtar, Raghad Z. Yousif, Yazen S. Almashhadani
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