Classification of Chest X-Ray Images using Wavelet and MFCC Features and Support Vector Machine Classifier
Received: 2 March 2021 | Revised: 31 March 2021 | Accepted: 11 April 2021 | Online: 21 August 2021
Corresponding author: H. A. Owida
Abstract
The shortage and availability limitation of RT-PCR test kits and is a major concern regarding the COVID-19 pandemic. The authorities' intention is to establish steps to control the propagation of the pandemic. However, COVID-19 is radiologically diagnosable using x-ray lung images. Deep learning methods have achieved cutting-edge performance in medical diagnosis software assistance. In this work, a new diagnostic method for detecting COVID-19 disease is implemented using advanced deep learning. Effective features were extracted using wavelet analysis and Mel Frequency Cepstral Coefficients (MFCC) method, and they used in the classification process using the Support Vector Machine (SVM) classifier. A total of 2400 X-ray images, 1200 of them classified as Normal (healthy) and 1200 as COVID-19, have been derived from a combination of public data sets to verify the validity of the proposed model. The experimental results obtained an overall accuracy of 98.8% by using five wavelet features, where the classification using MFCC features, MFCC-delta, and MFCC-delta-delta features reached accuracy around 97% on average. The results show that the proposed model has reached the required level of success to be applicable in COVID 19 diagnosis.
Keywords:
chest X-ray images, classification of chest diseases, covid-19, feature extraction, Support Vector Machine (SVM)Downloads
References
J. F.-W. Chan et al., "A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster," The Lancet, vol. 395, no. 10223, pp. 514-523, Feb. 2020. https://doi.org/10.1016/S0140-6736(20)30154-9
V. M. Corman et al., "Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR," Eurosurveillance, vol. 25, no. 3, Jan. 2020, Art. no. 2000045. https://doi.org/10.2807/1560-7917.ES.2020.25.21.2001035
O. Vandenberg, D. Martiny, O. Rochas, A. van Belkum, and Z. Kozlakidis, "Considerations for diagnostic COVID-19 tests," Nature Reviews Microbiology, vol. 19, no. 3, pp. 171-183, Mar. 2021. https://doi.org/10.1038/s41579-020-00461-z
L. Wang, Z. Q. Lin, and A. Wong, "COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images," Scientific Reports, vol. 10, no. 1, Nov. 2020, Art. no. 19549. https://doi.org/10.1038/s41598-020-76550-z
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," Computers in Biology and Medicine, vol. 121, Jun. 2020, Art. no. 103792. https://doi.org/10.1016/j.compbiomed.2020.103792
I. D. Apostolopoulos and T. A. Mpesiana, "Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks," Physical and Engineering Sciences in Medicine, vol. 43, no. 2, pp. 635-640, Jun. 2020. https://doi.org/10.1007/s13246-020-00865-4
B. Ghoshal, A. Tucker, B. Sanghera, and W. L. Wong, "Estimating uncertainty in deep learning for reporting confidence to clinicians in medical image segmentation and diseases detection," Computational Intelligence, vol. 37, no. 2, pp. 701-734, 2021. https://doi.org/10.1111/coin.12411
D. Lv, W. Qi, Y. Li, L. Sun, and Y. Wang, "A cascade network for Detecting COVID-19 using chest x-rays," arXiv:2005.01468 [cs, eess], May 2020, Accessed: Jun. 03, 2021. [Online]. Available: http://arxiv.org/abs/2005.01468.
I. Katsamenis, E. Protopapadakis, A. Voulodimos, A. Doulamis, and N. Doulamis, "Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images," in 24th Pan-Hellenic Conference on Informatics, New York, NY, USA, Nov. 2020, pp. 170-174. https://doi.org/10.1145/3437120.3437300
A. M. Ismael and A. Şengür, "Deep learning approaches for COVID-19 detection based on chest X-ray images," Expert Systems with Applications, vol. 164, Feb. 2021, Art. no. 114054. https://doi.org/10.1016/j.eswa.2020.114054
E. Hussain, M. Hasan, M. A. Rahman, I. Lee, T. Tamanna, and M. Z. Parvez, "CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images," Chaos, Solitons & Fractals, vol. 142, Jan. 2021, Art. no. 110495. https://doi.org/10.1016/j.chaos.2020.110495
P. Afshar, S. Heidarian, F. Naderkhani, A. Oikonomou, K. N. Plataniotis, and A. Mohammadi, "COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images," Pattern Recognition Letters, vol. 138, pp. 638-643, Oct. 2020. https://doi.org/10.1016/j.patrec.2020.09.010
A. Saleh, "A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images," Current Medical Imaging, vol. 17, no. 1, pp. 109-119, Dec. 2020. https://doi.org/10.2174/1573405616666200604163954
P. Sethy, K. Santi, Behera, P. Kumar, and P. Biswas, "Detection of coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine," vol. 5, no. 4, pp. 643-651, Apr. 2020. https://doi.org/10.33889/IJMEMS.2020.5.4.052
W. Helali, Ζ. Hajaiej, and A. Cherif, "Real Time Speech Recognition based on PWP Thresholding and MFCC using SVM," Engineering, Technology & Applied Science Research, vol. 10, no. 5, pp. 6204-6208, Oct. 2020. https://doi.org/10.48084/etasr.3759
A. Al-Ghraibah, M. Algharibeh, W. Al-Muhtaseb, F. Al-Khateeb, and I. Al-Anis, "Investigating the Significance of New Features Extracted from Long Bones X-ray Images," in 2020 IEEE 5th Middle East and Africa Conference on Biomedical Engineering (MECBME), Amman, Jordan, Oct. 2020. https://doi.org/10.1109/MECBME47393.2020.9265163
R. C. Gonzalez and R. E. Woods, Digital image processing. New York, NY, USA: Pearson, 2018.
A. A. Alasadi, T. H. Aldhayni, R. R. Deshmukh, A. H. Alahmadi, and A. S. Alshebami, "Efficient Feature Extraction Algorithms to Develop an Arabic Speech Recognition System," Engineering, Technology & Applied Science Research, vol. 10, no. 2, pp. 5547-5553, Apr. 2020. https://doi.org/10.48084/etasr.3465
S. Gupta, J. Jaafar, W. F. wan Ahmad, and A. Bansal, "Feature Extraction Using MFCC," Signal & Image Processing : An International Journal, vol. 4, no. 4, pp. 101-108, Aug. 2013. https://doi.org/10.5121/sipij.2013.4408
F. S. Cabral, H. Fukai, and S. Tamura, "Feature Extraction Methods Proposed for Speech Recognition Are Effective on Road Condition Monitoring Using Smartphone Inertial Sensors," Sensors, vol. 19, no. 16, Jan. 2019, Art. no. 3481. https://doi.org/10.3390/s19163481
T. Grzywalski et al., "Parameterization of Sequence of MFCCs for DNN-based voice disorder detection," in 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, Dec. 2018, pp. 5247-5251. https://doi.org/10.1109/BigData.2018.8622012
T. M. Mitchell, Machine learning. New York, NY, USA: Mcgraw Hill, 2017
A. Al-Ghraibah, L. E. Boucheron, and R. T. J. McAteer, "An automated classification approach to ranking photospheric proxies of magnetic energy build-up," Astronomy & Astrophysics, vol. 579, Jun. 2015, Art. No. A64. https://doi.org/10.1051/0004-6361/201525978
M Tamer Ozsu and L. Liu, Encyclopedia of database systems. New York, NY, USA: Springer, 2009.
A. Tharwat, "Classification assessment methods," Applied Computing and Informatics, vol. 17, no. 1, pp. 168-192, Jan. 2020. https://doi.org/10.1016/j.aci.2018.08.003
M. Kalechman, Practical MATLAB basics for engineers. Boca Raton, FL, USA: Crc Press, 2018. https://doi.org/10.1201/9781420047752
"Chest X-Ray Images (Pneumonia)," Kaggle. https://kaggle.com/paultimothymooney/chest-xray-pneumonia (accessed Jun. 03, 2021).
Downloads
How to Cite
License
Copyright (c) 2021 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
