Research Article
Year 2022,
Volume: 17 Issue: 2, 299 - 308, 30.09.2022
Abstract
References
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- T. Ojala, M. Pietikäinen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern recognition, vol. 29, no. 1, pp. 51-59, 1996.
- T. Ojala and M. Pietikäinen, "Unsupervised texture segmentation using feature distributions," Pattern recognition, vol. 32, no. 3, pp. 477-486, 1999.
- X. Li, W. Tan, P. Liu, Q. Zhou, and J. Yang, "Classification of COVID-19 chest CT images based on ensemble deep learning," Journal of Healthcare Engineering, vol. 2021, 2021.
- D. M. Powers, "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation," arXiv preprint arXiv:2010.16061, 2020.
- M. J. Warrens, "On the equivalence of Cohen’s kappa and the Hubert-Arabie adjusted Rand index," Journal of classification, vol. 25, no. 2, pp. 177-183, 2008.
- X. Deng, Q. Liu, Y. Deng, and S. Mahadevan, "An improved method to construct basic probability assignment based on the confusion matrix for classification problem," Information Sciences, vol. 340, pp. 250-261, 2016.
Classification of Chest X-ray COVID-19 Images Using the Local Binary Pattern Feature Extraction Method
Abstract
Background and Purpose: COVID-19, which started in December 2019, caused significant loss of life and economic losses. Early diagnosis of the COVID-19 is important to reduce the risk of death. Therefore, studies have increased to detect COVID-19 with machine learning methods automatically. Materials and Methods: In this study, the dataset consists of 15153 X-ray images for 4961 patient cases in three classes: Viral Pneumonia, Normal and COVID-19. Firstly, the dataset was preprocessed. And then, the dataset was given to the Cubic Support Vector Machine (Cubic SVM), Linear Discriminant (LD), Quadratic Discriminant (QD), Ensemble, Kernel Naive Bayes (KNB), K-Nearest Neighbor Weighted (KNN Weighted) classification methods as input data. Then, the Local Binary Model (LBP) texture operator was applied for feature extraction. Results: These values were increased from 94.1% (without LBP) to 98.05% using the LBP method. The Cubic SVM method's highest accuracy was observed in these two applications. Conclusions: This study demonstrates that the performance of the presented methods with LBP feature extraction is improved.
Keywords
: Covid-19 local binary pattern feature extraction machine learning classification Covid-19
References
- L. Wynants et al., "Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal," bmj, vol. 369, 2020.
- D. Uphade and A. Muley, "Identification of parameters for classification of COVID-19 patient’s recovery days using machine learning techniques," J. Math. Comput. Sci., vol. 12, no. 3, p. Article ID 56, 2022.
- S. H. Kassania, P. H. Kassanib, M. J. Wesolowskic, K. A. Schneidera, and R. Detersa, "Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: a machine learning based approach," Biocybernetics and Biomedical Engineering, vol. 41, no. 3, pp. 867-879, 2021.
- J. N. Hasoon et al., "COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images," Results in Physics, vol. 31, p. 105045, 2021.
- M. Jawahar et al., "Diagnosis of covid-19 using optimized pca based local binary pattern features," International Journal of Current Research and Review, pp. 37-41, 2021.
- T. Tuncer, S. Dogan, and F. Ozyurt, "An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image," Chemometrics and Intelligent Laboratory Systems, vol. 203, p. 104054, 2020.
- P. P. Lakshmi, M. Sivagami, and V. Balaji, "A novel LT-LBP based prediction model for COVID-CT images with Machine Learning," in 2021 International Conference on Information Systems and Advanced Technologies (ICISAT), 2021: IEEE, pp. 1-5.
- H. Alquran, M. Alsleti, R. Alsharif, I. A. Qasmieh, A. M. Alqudah, and N. H. B. Harun, "Employing texture features of chest x-ray images and machine learning in covid-19 detection and classification," in Mendel, 2021, vol. 27, no. 1, pp. 9-17.
- M. Abed et al., "A comprehensive investigation of machine learning feature extraction and classification methods for automated diagnosis of COVID-19 based on X-ray images," Computers, Materials, & Continua, pp. 3289-3310, 2021.
- M. Barstugan, U. Ozkaya, and S. Ozturk, "Coronavirus (covid-19) classification using ct images by machine learning methods," arXiv preprint arXiv:2003.09424, 2020.
- L. N. Rohmah and A. Bustamam, "Improved classification of coronavirus disease (covid-19) based on combination of texture features using ct scan and x-ray images," in 2020 3rd International Conference on Information and Communications Technology (ICOIACT), 2020: IEEE, pp. 105-109.
- N. Amini and A. Shalbaf, "Automatic classification of severity of COVID‐19 patients using texture feature and random forest based on computed tomography images," International Journal of Imaging Systems and Technology, vol. 32, no. 1, pp. 102-110, 2022.
- S. Balakrishnama and A. Ganapathiraju, "Linear discriminant analysis-a brief tutorial," Institute for Signal and information Processing, vol. 18, no. 1998, pp. 1-8, 1998.
- S. Srivastava, M. R. Gupta, and B. A. Frigyik, "Bayesian quadratic discriminant analysis," Journal of Machine Learning Research, vol. 8, no. 6, 2007.
- Y. Ren, L. Zhang, and P. N. Suganthan, "Ensemble classification and regression-recent developments, applications and future directions," IEEE Computational intelligence magazine, vol. 11, no. 1, pp. 41-53, 2016.
- U. Jain, K. Nathani, N. Ruban, A. N. J. Raj, Z. Zhuang, and V. G. Mahesh, "Cubic SVM classifier based feature extraction and emotion detection from speech signals," in 2018 International Conference on Sensor Networks and Signal Processing (SNSP), 2018: IEEE, pp. 386-391.
- T. Rahman, M. Chowdhury, and A. Khandakar. "COVID-19 Radiography Database, COVID-19 Chest X-ray Database, https://www.kaggle.com/tawsifurrahman/covid19-radiography-database." (accessed.
- T. Ojala, M. Pietikäinen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern recognition, vol. 29, no. 1, pp. 51-59, 1996.
- T. Ojala and M. Pietikäinen, "Unsupervised texture segmentation using feature distributions," Pattern recognition, vol. 32, no. 3, pp. 477-486, 1999.
- X. Li, W. Tan, P. Liu, Q. Zhou, and J. Yang, "Classification of COVID-19 chest CT images based on ensemble deep learning," Journal of Healthcare Engineering, vol. 2021, 2021.
- D. M. Powers, "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation," arXiv preprint arXiv:2010.16061, 2020.
- M. J. Warrens, "On the equivalence of Cohen’s kappa and the Hubert-Arabie adjusted Rand index," Journal of classification, vol. 25, no. 2, pp. 177-183, 2008.
- X. Deng, Q. Liu, Y. Deng, and S. Mahadevan, "An improved method to construct basic probability assignment based on the confusion matrix for classification problem," Information Sciences, vol. 340, pp. 250-261, 2016.
There are 23 citations in total.
Details
| Primary Language | English |
|---|---|
| Journal Section | TJST |
| Authors | |
| Publication Date | September 30, 2022 |
| Submission Date | March 24, 2022 |
| Published in Issue | Year 2022 Volume: 17 Issue: 2 |
