A Comprehensive Review on COVID-19 Cough Audio Classification through Deep Learning

Authors

  • Praveen Gupta  Research Scholar, Dept. of Computer Engineering, Sigma Institute of Engineering, Gujarat, India
  • Sheshang Degadwala  Associate Professor & Head of Department, Dept. of Computer Engineering, Sigma University, Gujarat, India

DOI:

https://doi.org/10.32628/CSEIT2361049

Keywords:

COVID-19, Cough audio classification, Deep learning, Convolutional Neural Networks, Recurrent Neural Networks, Feature extraction, Diagnostic tools.

Abstract

This review paper provides a comprehensive analysis of the advancements in COVID-19 cough audio classification through deep learning techniques. With the ongoing global pandemic, there is a growing need for non-intrusive and rapid diagnostic tools, and the utilization of audio-based methods for COVID-19 detection has gained considerable attention. The paper systematically reviews and compares various deep learning models, methodologies, and datasets employed for COVID-19 cough audio classification. The effectiveness, challenges, and future directions of these approaches are discussed, shedding light on the potential of audio-based diagnostics in the context of the current public health crisis.

References

  1. S. Ulukaya, A. A. Sarıca, O. Erdem, and A. Karaali, “MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds,” Medical and Biological Engineering and Computing, vol. 61, no. 7, pp. 1619–1629, 2023, doi: 10.1007/s11517-023-02803-4.
  2. S. Kim, J. Y. Baek, and S. P. Lee, “COVID-19 Detection Model with Acoustic Features from Cough Sound and Its Application,” Applied Sciences (Switzerland), vol. 13, no. 4, 2023, doi: 10.3390/app13042378.
  3. S. A. Almutairi, “A multimodal AI-based non-invasive COVID-19 grading framework powered by deep learning, manta ray, and fuzzy inference system from multimedia vital signs,” Heliyon, vol. 9, no. 6, p. e16552, 2023, doi: 10.1016/j.heliyon.2023.e16552.
  4. N. K. Chowdhury, M. A. Kabir, M. M. Rahman, and S. M. S. Islam, “Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method,” Computers in Biology and Medicine, vol. 145, no. March, p. 105405, 2022, doi: 10.1016/j.compbiomed.2022.105405.
  5. T. Hoang, L. Pham, D. Ngo, and H. D. Nguyen, “A Cough-based deep learning framework for detecting COVID-19,” Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2022-July, no. 1, pp. 3422–3425, 2022, doi: 10.1109/EMBC48229.2022.9871179.
  6. M. Aly and N. S. Alotaibi, “A novel deep learning model to detect COVID-19 based on wavelet features extracted from Mel-scale spectrogram of patients’ cough and breathing sounds,” Informatics in Medicine Unlocked, vol. 32, no. June, p. 101049, 2022, doi: 10.1016/j.imu.2022.101049.
  7. A. E. Ashby et al., “Cough-based COVID-19 detection with audio quality clustering and confidence measure based learning Khuong An Nguyen,” Proceedings of Machine Learning Research, vol. 179, no. Ml, pp. 1–20, 2022.
  8. M. Pahar et al., “Automatic Tuberculosis and COVID-19 cough classification using deep learning,” International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022, no. July, pp. 20–22, 2022, doi: 10.1109/ICECET55527.2022.9873469.
  9. O. O. Abayomi-Alli, R. Damaševičius, A. A. Abbasi, and R. Maskeliūnas, “Detection of COVID-19 from Deep Breathing Sounds Using Sound Spectrum with Image Augmentation and Deep Learning Techniques,” Electronics (Switzerland), vol. 11, no. 16, 2022, doi: 10.3390/electronics11162520.
  10. Z. Ren, Y. Chang, W. Nejdl, and B. W. Schuller, “Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition,” Acta Acustica, vol. 6, pp. 0–4, 2022, doi: 10.1051/aacus/2022029.
  11. E. A. Mohammed, M. Keyhani, A. Sanati-Nezhad, S. H. Hejazi, and B. H. Far, “An ensemble learning approach to digital corona virus preliminary screening from cough sounds,” Scientific Reports, vol. 11, no. 1, pp. 1–11, 2021, doi: 10.1038/s41598-021-95042-2.
  12. Y. Chang, X. Jing, Z. Ren, and B. W. Schuller, “CovNet: A Transfer Learning Framework for Automatic COVID-19 Detection From Crowd-Sourced Cough Sounds,” Frontiers in Digital Health, vol. 3, no. August 2021, pp. 1–11, 2022, doi: 10.3389/fdgth.2021.799067.
  13. S. Rao, V. Narayanaswamy, M. Esposito, J. J. Thiagarajan, and A. Spanias, “COVID-19 detection using cough sound analysis and deep learning algorithms,” Intelligent Decision Technologies, vol. 15, no. 4, pp. 655–665, 2021, doi: 10.3233/IDT-210206.
  14. M. Pahar, M. Klopper, R. Warren, and T. Niesler, “COVID-19 cough classification using machine learning and global smartphone recordings,” Computers in Biology and Medicine, vol. 135, no. June, p. 104572, 2021, doi: 10.1016/j.compbiomed.2021.104572.
  15. M. Loey and S. Mirjalili, “COVID-19 cough sound symptoms classification from scalogram image representation using deep learning models Mohamed,” Computers in Biology and Medicine, no. January, 2021.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2023-10-30

Issue

Volume 9, Issue 10, September-October-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Praveen Gupta, Sheshang Degadwala, " A Comprehensive Review on COVID-19 Cough Audio Classification through Deep Learning" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 10, pp.289-294, September-October-2023. Available at doi : https://doi.org/10.32628/CSEIT2361049