Revisiting COVID-19 Diagnosis From Cough Sound: A Hybrid CNN-LSTM Model Utilizing Offline Time Stretching Augmentation
Authors: 
Yijia Guan, Rosa ChanAuthors Info & Claims
Yijia Guan, Rosa ChanAuthors Info & ClaimsPages 230 - 237
Abstract
The global outbreak of the COVID-19 pandemic has driven the development of effective and low-cost detection technologies. With an emphasis on methods' economic viability and detection efficacy, researchers have been actively exploring novel technologies in response to it. To address the issue, we have revisited a deep learning-based framework to facilitate the diagnosis of COVID-19 solely through the analysis of cough sounds. We utilized the label of expert diagnoses and employed time stretching as an augmentation method on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM). Trained and Tested on the largest publicly-available COUGHVID dataset, our proposed hybrid CNN-LSTM model showed its performance with a short training period, demonstrating proficiency in discerning between COVID-19-related cough sounds and those of a healthy nature. Our classification model achieved an accuracy of 99.19%, a precision of 94.92%, a recall of 88.61%, a F1 Score of 91.66%, and an AUC score of 96%.
References
[1]
Vipin Bansal, Gaurav Pahwa, and Nirmal Kannan. 2020. Cough Classification for COVID-19 based on audio mfcc features using Convolutional Neural Networks. In 2020 IEEE international conference on computing, power and communication technologies (GUCON). IEEE, 604–608.
[2]
Gaffari Celik. 2023. CovidCoughNet: A new method based on convolutional neural networks and deep feature extraction using pitch-shifting data augmentation for covid-19 detection from cough, breath, and voice signals. Computers in Biology and Medicine 163 (2023), 107153. https://doi.org/10.1016/j.compbiomed. 2023.107153
[3]
Ankan Ghosh Dastider, Farhan Sadik, and Shaikh Anowarul Fattah. 2021. An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound. Computers in Biology and Medicine 132 (2021), 104296.
[4]
Julia Diaz-Escobar, Nelson E Ordonez-Guillen, Salvador Villarreal-Reyes, Alejandro Galaviz-Mosqueda, Vitaly Kober, Raúl Rivera-Rodriguez, and Jose E Lozano Rizk. 2021. Deep-learning based detection of COVID-19 using lung ultrasound imagery. Plos one 16, 8 (2021), e0255886.
[5]
Chun Shuang Guan, Zhi Bin Lv, Shuo Yan, Yan Ni Du, Hui Chen, Lian Gui Wei, Ru Ming Xie, and Bu Dong Chen. 2020. Imaging features of coronavirus disease 2019 (COVID-19): evaluation on thin-section CT. Academic radiology 27, 5 (2020), 609–613.
[6]
Skander Hamdi, Mourad Oussalah, Abdelouahab Moussaoui, and Mohamed Saidi. 2022. Attention-based hybrid CNN-LSTM and spectral data augmentation for COVID-19 diagnosis from cough sound. Journal of Intelligent Information Systems 59, 2 (2022), 367–389.
[7]
Md Rashidul Hasan, Mustafa Jamil, MGRMS Rahman, 2004. Speaker identification using mel frequency cepstral coefficients. variations 1, 4 (2004), 565–568.
[8]
Abdelfatah Hassan, Ismail Shahin, and Mohamed Bader Alsabek. 2020. Covid-19 detection system using recurrent neural networks. In 2020 International conference on communications, computing, cybersecurity, and informatics (CCCI). IEEE, 1–5.
[9]
David S Hui, Esam I Azhar, Tariq A Madani, Francine Ntoumi, Richard Kock, Osman Dar, Giuseppe Ippolito, Timothy D Mchugh, Ziad A Memish, Christian Drosten, 2020. The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health—The latest 2019 novel coronavirus outbreak in Wuhan, China. International journal of infectious diseases 91 (2020), 264–266.
[10]
Ali Imran, Iryna Posokhova, Haneya N Qureshi, Usama Masood, Muhammad Sajid Riaz, Kamran Ali, Charles N John, MD Iftikhar Hussain, and Muhammad Nabeel. 2020. AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Informatics in medicine unlocked 20 (2020), 100378.
[11]
Stephen A Lauer, Kyra H Grantz, Qifang Bi, Forrest K Jones, Qulu Zheng, Hannah R Meredith, Andrew S Azman, Nicholas G Reich, and Justin Lessler. 2020. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Annals of internal medicine 172, 9 (2020), 577–582.
[12]
Mohamed Loey and Seyedali Mirjalili. 2021. COVID-19 cough sound symptoms classification from scalogram image representation using deep learning models. Computers in biology and medicine 139 (2021), 105020.
[13]
Antonios Makris, Ioannis Kontopoulos, and Konstantinos Tserpes. 2020. COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks. In 11th hellenic conference on artificial intelligence. 60–66.
[14]
Zohaib Mushtaq and Shun-Feng Su. 2020. Environmental sound classification using a regularized deep convolutional neural network with data augmentation. Applied Acoustics 167 (2020), 107389.
[15]
Ali Bou Nassif, Ismail Shahin, Mohamed Bader, Abdelfatah Hassan, and Naoufel Werghi. 2022. COVID-19 detection systems using deep-learning algorithms based on speech and image data. Mathematics 10, 4 (2022), 564.
[16]
Mina A Nessiem, Mostafa M Mohamed, Harry Coppock, Alexander Gaskell, and Björn W Schuller. 2021. Detecting COVID-19 from breathing and coughing sounds using deep neural networks. In 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 183–188.
[17]
World Health Organization. 2021. Coronavirus disease (COVID-19). Online. https://www.who.int/health-topics/coronavirus#tab=tab_1
[18]
Lara Orlandic, Tomas Teijeiro, and David Atienza. 2021. The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms. Scientific Data 8, 1 (2021), 156.
[19]
Madhurananda Pahar, Marisa Klopper, Robin Warren, and Thomas Niesler. 2021. COVID-19 cough classification using machine learning and global smartphone recordings. Computers in Biology and Medicine 135 (2021), 104572.
[20]
Samritika Thakur and Aman Kumar. 2021. X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN). Biomedical Signal Processing and Control 69 (2021), 102920.
[21]
Wentao Zhao, Wei Jiang, and Xinguo Qiu. 2021. Deep learning for COVID-19 detection based on CT images. Scientific Reports 11, 1 (2021), 14353.
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- Revisiting COVID-19 Diagnosis From Cough Sound: A Hybrid CNN-LSTM Model Utilizing Offline Time Stretching Augmentation
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Published In
November 2023
295 pages
ISBN:9798400708343
DOI:10.1145/3637732
Copyright © 2023 ACM.
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Published: 28 February 2024
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ICBBE 2023
ICBBE 2023: 2023 10th International Conference on Biomedical and Bioinformatics Engineering
November 9 - 12, 2023
Kyoto, Japan
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