Skip to main content
SpringerLink
Log in

Long Short-Term Memory-based Deep Learning Model for COVID-19 Detection using Coughing Sound

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

The sudden spike in Coronavirus disease 2019 (COVID-19) cases reported by China recently suggests the possible threat of the pandemic’s resurgence. Due to the extremely contagious nature of the virus COVID-19 has already made its presence felt in countries all over the world, since it was reported first in 2019. The delay in detection of the infection resulted in daunting figures of infected patients and over six million fatal casualties have been reported to date. Necessitated by the urgent need for early detection of the disease to contain its spread, and the cost and time implication of the existing testing options, researchers have tried to provide AI-based systems which majorly exploit chest X-ray and Computed Tomography (CT) scan images. Our work explores the possibility of a real-time non-invasive screening tool for early detection of the disease through audio data like cough, resulting from respiratory tract infection which is an early symptom of COVID-19. In this paper, we have proposed a Long Short-Term Memory (LSTM)-based Deep Learning approach for the detection of COVID-19, using the Pfizer Digital Medicine Challenge dataset which contains audio signals of sneezing, coughing, and breath, classified as sick or not sick. Our proposed model obtained a training accuracy of 96.74% with a binary cross-entropy loss of 8.04%. The experimental results show an accuracy of 96.24% which is significantly higher than the previous study on the same dataset which resulted in an accuracy of 80.26%, suggesting that the proposed model is better in terms of classification efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The dataset that supports the findings of this work is available on the “OSFHOME” open-access platform at https://osf.io/tmkud/.

References

  1. Rahman T, Ibtehaz N, Khandakar A, Hossain MSA, Mekki YMS, Ezeddin M, Bhuiyan EH, Ayari MA, Tahir A, Qiblawey Y, Mahmud S, Zughaier SM, Abbas T, Al-Maadeed S, Chowdhury MEH. QUCoughScope: An intelligent application to detect COVID-19 patients using cough and breath sounds. MDPI. 2022. Retrieved April 7, 2022, from https://www.mdpi.com/2075-4418/12/4/920

  2. Laguarta J, Hueto F, Subirana B. Covid-19 artificial intelligence diagnosis using only cough recordings. IEEE Open J Eng Med Biol. 2020;1:275–81. https://doi.org/10.1109/ojemb.2020.3026928.

    Article  Google Scholar 

  3. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80. https://doi.org/10.1162/neco.1997.9.8.1735.

    Article  Google Scholar 

  4. Elpeltagy M, Sallam H. Automatic prediction of COVID− 19 from chest images using modified Resnet50. Multimed Tools Appl. 2021. https://doi.org/10.1007/s11042-021-10783-6.

    Article  Google Scholar 

  5. Jaiswal A, Gianchandani N, Singh D, Kumar V, Kaur M. Classification of the COVID-19 infected patients using DENSENET201 based deep transfer learning. J Biomol Struct Dyn. 2020;39(15):5682–9. https://doi.org/10.1080/07391102.2020.1788642.

    Article  Google Scholar 

  6. Acar E, Yilmaz İ. Covid-19 detection on IBM quantum computer with classical-quantum transfer learning. Turk J Electr Eng Comput Sci. 2021;29(1):46–61. https://doi.org/10.3906/elk-2006-94.

    Article  Google Scholar 

  7. Kundu R, Singh PK, Ferrara M, Ahmadian A, Sarkar R. ET-net: an ensemble of transfer learning models for prediction of COVID-19 infection through chest CT-scan images. Multimed Tools Appl. 2021;81(1):31–50. https://doi.org/10.1007/s11042-021-11319-8.

    Article  Google Scholar 

  8. Gifani P, Shalbaf A, Vafaeezadeh M. Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans. Int J Comput Assist Radiol Surg. 2020;16(1):115–23. https://doi.org/10.1007/s11548-020-02286-w.

    Article  Google Scholar 

  9. Aslan MF, Unlersen MF, Sabanci K, Durdu A. CNN-based Transfer Learning–BILSTM network: a novel approach for COVID-19 infection detection. Appl Soft Comput. 2021;98:106912. https://doi.org/10.1016/j.asoc.2020.106912.

    Article  Google Scholar 

  10. Chakraborty S, Paul S, Hasan KM. A transfer learning-based approach with deep CNN for Covid-19- and pneumonia-affected chest X-ray image classification. SN Comput Sci. 2021. https://doi.org/10.1007/s42979-021-00881-5.

    Article  Google Scholar 

  11. Rahman MDA, Hossain MS, Alrajeh NA, Gupta BB. A multimodal, multimedia point-of-care deep learning framework for covid-19 diagnosis. ACM Trans Multimed Comput Commun Appl. 2021;17(1s):1–24. https://doi.org/10.1145/3421725.

    Article  Google Scholar 

  12. Elzeki OM, Shams M, Sarhan S, Abd Elfattah M, Hassanien AE. Covid-19: a new deep learning computer-aided model for classification. PeerJ Comput Sci. 2021. https://doi.org/10.7717/peerj-cs.358.

    Article  Google Scholar 

  13. Momeny M, Neshat AA, Hussain MA, Kia S, Marhamati M, Jahanbakhshi A, Hamarneh G. Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of covid-19 in X-ray images. Comput Biol Med. 2021;136:104704. https://doi.org/10.1016/j.compbiomed.2021.104704.

    Article  Google Scholar 

  14. Calderon-Ramirez S, Giri R, Yang S, Moemeni A, Umana M, Elizondo D, Torrents-Barrena J, Molina-Cabello MA. Dealing with scarce labelled data: semi-supervised deep learning with mix match for COVID-19 detection using chest X-ray images. 2020 25th Int Conf Pattern Recogn (ICPR). 2021. https://doi.org/10.1109/icpr48806.2021.9412946.

    Article  Google Scholar 

  15. Higenbottam T. Chronic cough and the cough reflex in common lung diseases. Pulm Pharmacol Ther. 2002;15(3):241–7. https://doi.org/10.1006/pupt.2002.0341.

    Article  Google Scholar 

  16. Windmon A, Minakshi M, Bharti P, Chellappan S, Johansson M, Jenkins BA, Athilingam PR. TussisWatch: a smart-phone system to identify cough episodes as early symptoms of chronic obstructive pulmonary disease and congestive heart failure. IEEE J Biomed Health Inform. 2019;23(4):1566–73. https://doi.org/10.1109/jbhi.2018.2872038.

    Article  Google Scholar 

  17. Deshpande G, Batliner A, Schuller BW. AI-based human audio processing for COVID-19: a comprehensive overview. Pattern Recogn. 2022;122:108289. https://doi.org/10.1016/j.patcog.2021.108289.

    Article  Google Scholar 

  18. Brown C, Chauhan J, Grammenos A, Han J, Hasthanasombat A, Spathis D, Xia T, Cicuta P, Mascolo C. Exploring automatic diagnosis of COVID-19 from crowdsourced respiratory sound data. Proc 26th ACM SIGKDD Int Conf Knowl Discov Data Min. 2020. https://doi.org/10.1145/3394486.3412865.

    Article  Google Scholar 

  19. Pahar M, Klopper M, Warren R, Niesler T. Covid-19 cough classification using machine learning and Global smartphone recordings. Comput Biol Med. 2021;135:104572. https://doi.org/10.1016/j.compbiomed.2021.104572.

    Article  Google Scholar 

  20. Khriji L, Ammari A, Messaoud S, Bouaafia S, Maraoui A, Machhout M. Covid-19 recognition based on patient’s coughing and breathing patterns analysis: deep learning approach. 2021 29th Conf Open Innov Assoc. 2021. https://doi.org/10.23919/fruct52173.2021.9435454.

    Article  Google Scholar 

  21. Patel S, Rivas A, Psaltos D. Dataset of sounds of symptoms associated with respiratory sickness. OSF. 2002. Retrieved February 11, 2022, from https://osf.io/tmkud/

  22. Nessiem MA, Mohamed MM, Coppock H, Gaskell A, Schuller BW. “Detecting COVID-19 from breathing and coughing sounds using deep neural networks.” Proceedings of the IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS). pp 183–188. https://doi.org/10.1109/CBMS52027.2021.00069

  23. Chaudhari G, Jiang X, Fakhry A, Han A, Xiao J, Shen S, Khanzada A. Virufy: Global applicability of crowdsourced and clinical datasets for AI detection of covid-19 from cough. (2021) arXiv.org. Retrieved August 14, 2022, from https://arxiv.org/abs/2011.13320

  24. Melek N. Responding to challenge call of machine learning model development in diagnosing respiratory disease sounds. DeepAI. 2021. https://doi.org/10.48550/arXiv.2111.14354.

    Article  Google Scholar 

  25. McFee B, Raffel C, Liang D, Ellis D, McVicar M, Battenberg E, Nieto O. Librosa: Audio and Music Signal Analysis in python. Proceedings of the 14th Python in Science Conference. 2015. https://doi.org/10.25080/majora-7b98e3ed-003

  26. Audio feature extraction—audio classification and keyword spotting. Coursera. (n.d.). Retrieved August 19, 2022, from https://www.coursera.org/lecture/introduction-to-embedded-machine-learning/audio-feature-extraction-VxDmo

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Information Technology, Pune, India

    Anjali Malviya, Rahul Dixit & Nagendra Kushwaha

  2. Sardar Vallabhbhai National Institute of Technology, Surat, India

    Anupam Shukla

Authors
  1. Anjali Malviya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rahul Dixit
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anupam Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nagendra Kushwaha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anjali Malviya.

Ethics declarations

Conflict of Interest

On behalf of all the authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Research Trends in Computational Intelligence” guest edited by Anshul Verma, Pradeepika Verma, Vivek Kumar Singh and S. Karthikeyan.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Malviya, A., Dixit, R., Shukla, A. et al. Long Short-Term Memory-based Deep Learning Model for COVID-19 Detection using Coughing Sound. SN COMPUT. SCI. 4, 505 (2023). https://doi.org/10.1007/s42979-023-01934-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-01934-7

Keywords

Search

Navigation