Predicting COVID-19 Cough Sounds Using Spectrogram Analysis Across Multiple Classes

Praveen Gupta Gupta; Dr. Sheshang Degadwala Degadwala

doi:10.32628/CSEIT2410221

Authors

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

DOI:

https://doi.org/10.32628/CSEIT2410221

Keywords:

Cough Sounds, Spectrogram, Multi-classification, Convolutional Neural Network, Hyper-Tuning, Transfer Learning

Abstract

The COVID-19 pandemic has underscored the need for effective diagnostic tools. One promising avenue involves analyzing cough sounds to glean insights into respiratory health. This study presents a new method for predicting COVID-19 cough sounds using spectrogram analysis across various classes. We leverage advanced deep learning models such as DenseNet121, VGG16, ResNet50, and Inception Net, alongside our novel CNN architecture, to extract pertinent features from cough sound spectrograms. We use a diverse dataset encompassing cough sounds from COVID-19 positive and negative cases, as well as other respiratory conditions, for model training and assessment. Our results demonstrate the effectiveness of our approach in accurately categorizing COVID-19 cough sounds, outperforming existing models. This methodology shows promise as a non-invasive, scalable, and economical tool for early COVID-19 detection and monitoring, aiding public health efforts during the pandemic.

Downloads

Download data is not yet available.

References

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. DOI: https://doi.org/10.1007/s11517-023-02803-4

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. DOI: https://doi.org/10.3390/app13042378

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. DOI: https://doi.org/10.1016/j.heliyon.2023.e16552

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. DOI: https://doi.org/10.1016/j.compbiomed.2022.105405

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. DOI: https://doi.org/10.1109/EMBC48229.2022.9871179

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. DOI: https://doi.org/10.1016/j.imu.2022.101049

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.

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. DOI: https://doi.org/10.1109/ICECET55527.2022.9873469

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. DOI: https://doi.org/10.3390/electronics11162520

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. DOI: https://doi.org/10.1051/aacus/2022029

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. DOI: https://doi.org/10.1038/s41598-021-95042-2

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. DOI: https://doi.org/10.3389/fdgth.2021.799067

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. DOI: https://doi.org/10.3233/IDT-210206

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. DOI: https://doi.org/10.1016/j.compbiomed.2021.104572

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. DOI: https://doi.org/10.1016/j.compbiomed.2021.105020