Spectrogram-based Deep Learning Approach for Anomaly Detection from Cough Sounds
Article
Article Title | Spectrogram-based Deep Learning Approach for Anomaly Detection from Cough Sounds |
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Article Category | Article |
Authors | Keles, Tugce, Dogan, Sengul, Hafeez-Baig, Abdul and Tuncer, Turker |
Editors | Matthew He |
Journal Title | International Journal of Information Technology and Computer Science (IJITCS) |
Journal Citation | 17 (3), pp. 1-12 |
Number of Pages | 12 |
Year | 2025 |
Publisher | Modern Education and Computer Science Press |
Place of Publication | Hongkong |
ISSN | 2074-9007 |
2074-9015 | |
Digital Object Identifier (DOI) | https://doi.org/10.5815/ijitcs.2025.03.01 |
Web Address (URL) | https://www.mecs-press.org/ijitcs/ijitcs-v17-n3/v17n3-1.html |
Abstract | Artificial intelligence is now applied in many fields beyond computer science. In healthcare, it enables early disease detection and improves patient outcomes. This study develops a model that uses AI to find abnormal patterns in cough sounds. A cough is a key symptom of asthma and other respiratory diseases. Previous research has focused on raw audio signals of coughs. In contrast, we analyze spectrogram images derived from these sounds to improve accuracy. We designed a new convolutional neural network (CNN) for this purpose and the recommended CNN is termed as TwoConvNeXt. To showcase the classification performance of the recommended TwoConvNeXt model, a cough sound dataset has been utilized and the recommended TwoConvNeXt achieved 99.66% classification test accuracy. |
Keywords | Artificial Intelligence; Asthma Detection; Deep Learning; Cough Sounds; Convolutional Neural Networks |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4602. Artificial intelligence |
Byline Affiliations | Firat University, Turkey |
School of Management and Enterprise |
https://research.usq.edu.au/item/zyq2w/spectrogram-based-deep-learning-approach-for-anomaly-detection-from-cough-sounds
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