COVID-19 Chest X-Ray Detection Performance Through Variations of Wavelets Basis Function
Keywords:
Chest X-Ray, Covid-19 Detection, Wavelet Family, Support Vector Machines, 10-Fold Cross Validation
Abstract
Our previous work regarding the X-Ray detection of COVID-19 using Haar wavelet feature extraction and the Support Vector Machines (SVM) classification machine has shown that the combination of the two methods can detect COVID-19 well but then the question arises whether the Haar wavelet is the best wavelet method. So that in this study we conducted experiments on several wavelet methods such as biorthogonal, coiflet, Daubechies, haar, and symlets for chest X-Ray feature extraction with the same dataset. The results of the feature extraction are then classified using SVM and measure the quality of the classification model with parameters of accuracy, error rate, recall, specification, and precision. The results showed that the Daubechies wavelet gave the best performance for all classification quality parameters. The Daubechies wavelet transformation gave 95.47% accuracy, 4.53% error rate, 98.75% recall, 92.19% specificity, and 93.45% precision.
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References
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[4] A. Sharma, D. Raju, and S. Ranjan, “Detection of pneumonia clouds in chest X-ray using image processing approach,” 2017 Nirma Univ. Int. Conf. Eng., vol. 20, no. 1, pp. 1–4, 2017, doi: 10.1109/NUICONE.2017.8325607.
[5] Y.-H. Chan, Y.-Z. Zeng, H.-C. Wu, M.-C. Wu, and H.-M. Sun, “Effective Pneumothorax Detection for Chest X-Ray Images Using Local Binary Pattern and Support Vector Machine,” J. Healthc. Eng., vol. 20, no. 1, pp. 98–104, 2018.
[6] R. H and A. T, “Feature Extraction of Chest X-ray Images and Analysis using PCA and kPCA,” Int. J. Electr. Comput. Eng., vol. 8, no. 5, pp. 3392–3398, 2018.
[7] T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. R. Acharya, “Automated detection of COVID-19 cases using deep neural networks with X-ray images,” Comput. Biol. Med., vol. 121, no. 4, pp. 103–109, 2020, doi: https://doi.org/10.1016/j.compbiomed.2020.103792.
[8] J. Too, A. R. Abdullah, and N. M. Saad, “Classification of Hand movements based on discrete wavelet transform and enhanced feature extraction,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 6, pp. 83–89, 2019, doi: 10.14569/ijacsa.2019.0100612.
[9] T. G. Krishna, K. V. N. Sunitha, and S. Mishra, “Detection and Classification of Brain Tumor from MRI Medical Image using Wavelet Transform and PSO based LLRBFNN Algorithm,” Int. J. Comput. Sci. Eng., vol. 6, no. 1, pp. 18–23, 2018.
[10] N. Hashim, S. Adebayo, K. Abdan, and M. Hanafi, “Comparative study of transform-based image texture analysis for the evaluation of banana quality using an optical backscattering system,” Postharvest Biol. Technol., vol. 135, no. 1, pp. 38–50, 2018, doi: https://doi.org/10.1016/j.postharvbio.2017.08.021.
[11] S.-H. Wang et al., “Multiple Sclerosis Detection Based on Biorthogonal Wavelet Transform, RBF Kernel Principal Component Analysis, and Logistic Regression,” IEEE Access, vol. 4, no. 1, pp. 7567–7576, 2016, doi: 10.1109/ACCESS.2016.2620996.
[12] N. W. S. Saraswati, N. W. Wardani, and I. G. A. A. D. Indradewi, “Detection of Covid Chest X-Ray using Wavelet and Support Vector Machines,” Int. J. Eng. Emerg. Technol., vol. 5, no. 2, pp. 116–121, 2020.
[13] M. Sheykhmousa, M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, and P. Ghamisi, “Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, no. 1, pp. 6308–6325, 2020.
[14] X. Yang, M. Han, H. Tang, Q. Li, and X. Luo, “Detecting Defects with Support Vector Machine in Logistics Packaging Boxes for Edge Computing,” IEEE Access, vol. 8, no. 1, pp. 64002–64010, 2020.
[15] C. Gui, “Analysis of imbalanced data set problem: The case of churn prediction for telecommunication,” Artif. Intell. Res., vol. 6, no. 2, p. 93, 2017, doi: https://doi.org/10.5430/air.v6n2p93.
[16] W. Castro, J. Oblitas, D.-L.-T. Miguel, C. Cotrina, K. Bazan, and H. Avila-George, “Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color Spaces,” IEEE Access, vol. 7, no. 1, pp. 27389–27400, 2019, doi: 10.1109/ACCESS.2019.2898223.
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[18] Y. A. Sari, A. G. Hapsani, S. Adinugroho, L. Hakim, and S. Mutrofin, “Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction,” Int. J. Artif. Intell. Res., vol. 4, no. 2, p. 95, 2021, doi: 10.29099/ijair.v4i2.165.
[19] I. Candradewi, A. Harjoko, and B. A. . Sumbodo, “Intelligent Traffic Monitoring Systems: Vehicle Type Classification Using Support Vector Machine,” Int. J. Artif. Intell. Res., vol. 5, no. 1, pp. 78–90, 2021.
[20] K. Padmavathi and K. Thangadurai, “Implementation of RGB and grayscale images in plant leaves disease detection - Comparative study,” Indian J. Sci. Technol., vol. 9, no. 6, pp. 4–9, 2016, doi: 10.17485/ijst/2016/v9i6/77739.
[21] R. . Gonzalez, R. . Woods, and S. . Eddins, Digital Image Processing Using MATLAB, 3rd ed. Gatesmark Publishing, 2020.
[22] H. Kutlu and E. Avcı, “A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks,” Sensors (Basel)., vol. 19, no. 9, 2019, doi: 10.3390/s19091992.
[23] D. Putra, Pengolahan Citra Digital. Yogyakarta: Andi Offset, 2010.
[24] L. Cheng, D. Li, X. Li, and S. Yu, “The Optimal Wavelet Basis Function Selection in Feature Extraction of Motor Imagery Electroencephalogram Based on Wavelet Packet Transformation,” IEEE Access, vol. 7, no. Cc, pp. 174465–174481, 2019, doi: 10.1109/ACCESS.2019.2953972.
[25] N. Li, F. He, W. Ma, R. Wang, and X. Zhang, “Wind Power Prediction of Kernel Extreme Learning Machine Based on Differential Evolution Algorithm and Cross Validation Algorithm,” IEEE Access, vol. 8, pp. 68874–68882, 2020, doi: 10.1109/ACCESS.2020.2985381.
[26] P. R. Sihombing and I. F. Yuliati, “Penerapan Metode Machine Learning dalam Klasifikasi Risiko Kejadian Berat Badan Lahir Rendah di Indonesia,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 20, no. 2, pp. 417–426, 2021, doi: 10.30812/matrik.v20i2.1174.
[27] F. D. Ananda and Y. Pristyanto, “Analisis Sentimen Pengguna Twitter Terhadap Layanan Internet Provider Menggunakan Algoritma Support Vector Machine,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 20, no. 2, pp. 407–416, 2021, doi: 10.30812/matrik.v20i2.1130.
[28] A. Sridharan, A. S. Remya Ajai, and S. Gopalan, “A Novel Methodology for the Classification of Debris Scars using Discrete Wavelet Transform and Support Vector Machine,” Procedia Comput. Sci., vol. 171, no. 2019, pp. 609–616, 2020, doi: 10.1016/j.procs.2020.04.066.
[29] B. J. Erickson, P. Korfiatis, Z. Akkus, and T. L. Kline, “Machine learning for medical imaging,” Radiographics, vol. 37, no. 2, pp. 505–515, 2017, doi: 10.1148/rg.2017160130.
[30] S. B. Rakhmetulayeva, K. S. Duisebekova, A. M. Mamyrbekov, D. K. Kozhamzharova, G. N. Astaubayeva, and K. Stamkulova, “Application of Classification Algorithm Based on SVM for Determining the Effectiveness of Treatment of Tuberculosis,” Procedia Comput. Sci., vol. 130, pp. 231–238, 2018, doi: 10.1016/j.procs.2018.04.034.
[31] M. Hasnain, M. F. Pasha, I. Ghani, M. Imran, M. Y. Alzahrani, and R. Budiarto, “Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking,” IEEE Access, vol. 8, pp. 90847–90861, 2020, doi: 10.1109/ACCESS.2020.2994222.
[32] A. Luque, A. Carrasco, A. Martín, and A. de las Heras, “The impact of class imbalance in classification performance metrics based on the binary confusion matrix,” Pattern Recognit., vol. 91, pp. 216–231, 2019, doi: 10.1016/j.patcog.2019.02.023.
[33] N. A. Melo Riveros, B. A. Cardenas Espitia, and L. E. Aparicio Pico, “Comparison between K-means and Self-Organizing Maps algorithms used for diagnosis spinal column patients,” Informatics Med. Unlocked, vol. 16, no. July, p. 100206, 2019, doi: 10.1016/j.imu.2019.100206.
Published
2021-11-26
How to Cite
Indradewi, I. G. A., Saraswati, N. W., & Wardani, N. (2021). COVID-19 Chest X-Ray Detection Performance Through Variations of Wavelets Basis Function. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(1), 31-42. https://doi.org/https://doi.org/10.30812/matrik.v21i1.1089
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