Detection of Covid Chest X-Ray using Wavelet and Support Vector Machines

Ni Wayan Sumartini Saraswati; Ni Wayan Wardani; I Gusti Ayu Agung Diatri Indradewi

doi:10.24843/IJEET.2020.v05.i02.p019

Ni Wayan Sumartini Saraswati STMIK STIKOM Indonesia
Ni Wayan Wardani
I Gusti Ayu Agung Diatri Indradewi STMIK STIKOM Indonesia

DOI: https://doi.org/10.24843/IJEET.2020.v05.i02.p019

Abstract

The study of digital image processing is still a hot topic in the realm of research, especially in medical research. The presence of various digital image processing methods and machine learning also contributes to the progress of research in this field. Detecting Covid Chest X-Ray is a prediction problem solving with a supervised classification method. In this study, the SVM method was chosen because it is proven to function as a good classifier as has been done in previous studies. Where previously the chest x-ray image feature extraction was carried out using wavelet transform. Feature extraction using wavelets has given the distinctive features of normal lung X-rays and distinguishes them from the distinctive features of Covid lung X-rays. The measurement results of the average classification model for the approximate, vertical, horizontal and diagonal dataset are 93.91% accuracy, 6.09% error rate, 98.75% recall, 89.06% specificity, and 91.26% precision. The vertical dataset is the best dataset to get a classification model because it has the best value in the accuracy and recall variables, but still provides good performance in measuring precision.

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References

[1] IEEE Staff, 2017 Nirma University International Conference on Engineering (NUiCONE). IEEE, 2017.
[2] Y. Chan, Y. Zeng, H. Wu, M. Wu, and H. Sun, ‘Effective Pneumothorax Detection for Chest X-Ray Images Using Local Binary Pattern and Support Vector Machine’, J. Healthc. Eng., vol. 2018, 2018.
[3] H. Roopa and T. Asha, ‘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.
[4] S. L. K. Yee and W. J. K. Raymond, ‘Pneumonia Diagnosis Using Chest X-ray Images and Machine Learning’, in ACM International Conference Proceeding Series, 2020, pp. 101–105.
[5] T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya, ‘Automated detection of COVID-19 cases using deep neural networks with X-ray images’, Comput. Biol. Med., vol. 121, 2020.
[6] A. Zotin, Y. Hamad, K. Simonov, and M. Kurako, ‘Lung boundary detection for chest X-ray images classification based on GLCM and probabilistic neural networks’, Procedia Comput. Sci., vol. 159, pp. 1439–1448, 2019.
[7] N. Hashim, S. E. 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, pp. 38–50, 2018.
[8] N. Ahmadi and M. Nilashi, ‘Iris Texture Recognition based on Multilevel 2-D Haar Wavelet Decomposition and Hamming Distance Approach’, J. Soft Comput. Decis. Support Syst., vol. 5, no. 3, 2018.
[9] R. A. Rizal, I. S. Girsang, and S. A. Prasetiyo, ‘Klasifikasi Wajah Menggunakan Support Vector Machine ( SVM )’, Ris. dan E-Jurnal Manaj. Inform. Komput., vol. 3, no. September, pp. 1–5, 2019.
[10] M. Athoillah, M. I. Irawan, and M. Imah, ‘Support Vector Machine Untuk Image Retrieval’, in Seminar Nasional Matematika dan Pendidikan Matematika, 2015, no. 978, pp. 279–287.
[11] D. F. Azid, B. Irawan, and C. Setianingsih, ‘PENERJEMAHAN HURUF CYRILLIC RUSIA KE HURUF LATIN MENGGUNAKAN ALGORITMA SVM (SUPPORT VECTOR MACHINE)’, in e-Proceeding of Engineering, 2017, vol. 4, no. 3, pp. 4007–4014.
[12] Neneng, K. Adi, and R. R. Isnanto, ‘Support Vector Machine Untuk Klasifikasi Citra Jenis Daging Berdasarkan Tekstur Menggunakan Ekstraksi Ciri Gray Level Co-Occurrence Matrices ( GLCM )’, J. Sist. Inf. Bisnis, vol. 01, pp. 1–10, 2016.
[13] N. W. S. Saraswati, Text mining dengan metode naïve bayes classifier dan support vector machines untuk sentiment analysis. Universitas Udayana, 2011.
[14] A. A. Kasim and M. Sudarsono, ‘Algoritma Support Vector Machine ( SVM ) untuk Klasifikasi Ekonomi Penduduk Penerima Bantuan Pemerintah di Kecamatan Simpang Raya Sulawesi Tengah’, in Seminar Nasional APTIKOM (SEMNASTIK), 2019, pp. 568–573.
[15] N. W. S. Saraswati, K. K. Widiartha, and L. P. A. Prapitasari, ‘Vector machine to predict student retention: A computerized approach’, in Journal of Physics: Conference Series, 2020.
[16] R. A. Gitasari, B. Hidayat, and S. Aulia, ‘Klasifikasi Penyakit Diabetes Retinopati Berdasarkan Citra Digital Dengan Menggunakan Metode Wavelet Dan Support Vector Machine’, E-Proceeding Eng., vol. 2, no. 1, pp. 510–514, 2015.
[17] N. W. S. Saraswati, ‘Transformasi Wavelet Dan Thresholding Pada Citra Menggunakan Matlab’, J. TSI, vol. 1, no. 2, 2010.
[18] D. Putra, Pengolahan Citra Digital. Yogyakarta: Andi Offset, 2010.