Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm
Abstract The global spread of COVID-19, particularly through cough symptoms, necessitates efficient diagnostic tools. COVID-19 patients exhibit unique cough sound patterns distinguishable from other respiratory conditions. This study proposes an advanced framework to detect and predict COVID-19 usin...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85140-w |
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| author | G. Ayappan S. Anila |
| author_facet | G. Ayappan S. Anila |
| author_sort | G. Ayappan |
| collection | DOAJ |
| description | Abstract The global spread of COVID-19, particularly through cough symptoms, necessitates efficient diagnostic tools. COVID-19 patients exhibit unique cough sound patterns distinguishable from other respiratory conditions. This study proposes an advanced framework to detect and predict COVID-19 using deep learning from cough audio signals. Audio data from the COUGHVID dataset undergo preprocessing through fuzzy gray level difference histogram equalization, followed by segmentation with a U-Net model. Key features are extracted via Zernike Moments (ZM) and Gray Level Co-occurrence Matrix (GLCM). The Enhanced Deep Neural Network (EDNN), tuned by the Coronavirus Herd Immunity Optimizer (CHIO), performs final prediction by minimizing error metrics. Comparative simulation results reveal that the proposed EDNN–CHIO model improves MSE by 25.35% and SMAPE by 42.06% over conventional models like PSO, WOA, and LSTM. The proposed approach demonstrates superior error reduction, highlighting its potential for effective COVID-19 detection. |
| format | Article |
| id | doaj-art-ef66bfab1a984064a7d95a616e807bcf |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ef66bfab1a984064a7d95a616e807bcf2025-08-20T01:49:41ZengNature PortfolioScientific Reports2045-23222025-01-0115112410.1038/s41598-025-85140-wAutomatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithmG. Ayappan0S. Anila1Department of Electronics and Communication Engineering, Sri Venkateswara College of EngineeringDepartment of Electronics and Communication Engineering, Sri Ramakrishna Institute of TechnologyAbstract The global spread of COVID-19, particularly through cough symptoms, necessitates efficient diagnostic tools. COVID-19 patients exhibit unique cough sound patterns distinguishable from other respiratory conditions. This study proposes an advanced framework to detect and predict COVID-19 using deep learning from cough audio signals. Audio data from the COUGHVID dataset undergo preprocessing through fuzzy gray level difference histogram equalization, followed by segmentation with a U-Net model. Key features are extracted via Zernike Moments (ZM) and Gray Level Co-occurrence Matrix (GLCM). The Enhanced Deep Neural Network (EDNN), tuned by the Coronavirus Herd Immunity Optimizer (CHIO), performs final prediction by minimizing error metrics. Comparative simulation results reveal that the proposed EDNN–CHIO model improves MSE by 25.35% and SMAPE by 42.06% over conventional models like PSO, WOA, and LSTM. The proposed approach demonstrates superior error reduction, highlighting its potential for effective COVID-19 detection.https://doi.org/10.1038/s41598-025-85140-wCOVID-19 detection and predictionCough audio signalsCoronavirus herd immunity optimizerEnhanced deep neural network |
| spellingShingle | G. Ayappan S. Anila Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm Scientific Reports COVID-19 detection and prediction Cough audio signals Coronavirus herd immunity optimizer Enhanced deep neural network |
| title | Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm |
| title_full | Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm |
| title_fullStr | Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm |
| title_full_unstemmed | Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm |
| title_short | Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm |
| title_sort | automatic detection and prediction of covid 19 in cough audio signals using coronavirus herd immunity optimizer algorithm |
| topic | COVID-19 detection and prediction Cough audio signals Coronavirus herd immunity optimizer Enhanced deep neural network |
| url | https://doi.org/10.1038/s41598-025-85140-w |
| work_keys_str_mv | AT gayappan automaticdetectionandpredictionofcovid19incoughaudiosignalsusingcoronavirusherdimmunityoptimizeralgorithm AT sanila automaticdetectionandpredictionofcovid19incoughaudiosignalsusingcoronavirusherdimmunityoptimizeralgorithm |