Shen J, Zhang X, Lu Y, et al. Novel audio characteristic-dependent feature extraction and data augmentation methods for cough-based respiratory disease classification. Comput Biol Med. 2024;179:108843. https://doi.org/10.1016/J.COMPBIOMED.2024.108843.

Article  PubMed  Google Scholar 

Huang K, Gu X, Yang T, Xu J, Yang L, Zhao J, et al. Prevalence and burden of chronic cough in China: a national cross-sectional study. ERJ Open Res. 2022;8:75–2022. https://doi.org/10.1183/23120541.00075-2022.

Article  Google Scholar 

Mehmet K, Datta PB, Tugce K, et al. GCLP: An automated asthma detection model based on global chaotic logistic pattern using cough sounds.Eng Appl Artif Intell 2024;127(PA). https://doi.org/10.1016/J.ENGAPPAI.2023.107184.

Roy A, Satija U. AsthmaSCELNet: A lightweight supervised contrastive embedding learning framework for asthma classification using lung sounds. Proceedings of Interspeech. 2023;2023:5431–5. https://doi.org/10.21437/Interspeech.2023-428.

Article  Google Scholar 

Roy A, Satija U. AsTFSONN: A unified framework based on time-frequency domain self-operational neural network for asthmatic lung sound classification. IEEE Int Symposium Med Meas Appl. (MeMeA). 2023;1–6. https://doi.org/10.1109/MeMeA57477.2023.10171911.

Vodnala N, Yarlagadda PS, Bhuvana KS, Ch M, Sailaja K. Novel deep learning approaches to differentiate asthma and COPD based on cough sounds. In 2024 Parul International Conference on Engineering and Technology (PICET), Vadodara, India, 2024;1–4. https://doi.org/10.1109/PICET60765.2024.10716083.

Varun BP, Jonnalagadda I, Vishnu Vardhan C, Srinivas M, Ullas S. A comparative analysis of machine learning classifiers for audio-based disease classification. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India; 2024. pp. 1–7. https://doi.org/10.1109/ICCCNT61001.2024.10724370.

Ćirić D, Bogunović Đ, Janković M, Miletić M. Breath sound acquisition in different environments.2024 11th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN), Nis, Serbia; 2024. pp. 1–6. https://doi.org/10.1109/IcETRAN62308.2024.10735425.

Maithani V, Rajput U, Singh M, Bhakuni VS, Sharma K, Manwal M. Machine learning application in lung cancer prediction: Insights from random forest. 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC), Gwalior, India; 2024. pp. 173–178. https://doi.org/10.1109/AIC61668.2024.10730856.

B KS, S S. Early stage pneumonia disease detection using machine learning techniques. 2024 Second International Conference on Advances in Information Technology (ICAIT), Chikkamagaluru, Karnataka, India; 2024. pp. 1–4. https://doi.org/10.1109/ICAIT61638.2024.10690386.

Kumar S, Shvetsov AV, Alsamhi SH. FuzzyGuard: A novel multimodal neuro-fuzzy framework for COPD early diagnosis. IEEE Internet of Things Journal. https://doi.org/10.1109/ICAIT61638.2024.10690386.

Cao X. Research on audio classification of COVID-19 based on Deep learning. Lanzhou Jiaotong University, 2023. https://doi.org/10.27205/d.cnki.gltec.2023.000520.

Manzella F, Pagliarini G, Sciavicco G, Stan IE. The voice of COVID-19: breath and cough recording classification with temporal decision trees and random forests. Artif Intell Med. 2023;137:102486. https://doi.org/10.1016/j.artmed.2022.102486.

Article  PubMed  PubMed Central  Google Scholar 

Kho SJ, Shiong BLC, Vong W-T, Boon LK, Pathmanathan MD, Rahman MABA, Xuan KP, Hanafi WNBW, Peariasamy KM, Hui PTH. Malaysian cough sound analysis and COVID-19 classification with deep learning. Intelligence-Based Medicine. 2024;9:100129. https://doi.org/10.1016/j.ibmed.2023.100129.

Article  Google Scholar 

Madhurananda P, 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 

Ghourabi M, Mourad-Chehade F, Soubra R, Chkeir A. Enhanced cough analysis using 1-dimensional CNN features for respiratory health diagnosis. IEEE International Symposium on Medical Measurements and Applications (MeMeA), Eindhoven, Netherlands; 2024. pp. 1–6. https://doi.org/10.1109/MeMeA60663.2024.10596786.

Amin SU, Abbas MS, Kim B, Jung Y, Seo S. Enhanced anomaly detection in pandemic surveillance videos: an attention approach with EfficientNet-B0 and CBAM integration. IEEE Access. 2024;12:162697–712. https://doi.org/10.1109/ACCESS.2024.3488797.

Article  Google Scholar 

Li Y. Research on cough event detection and classification based on Audio analysis technology. China Jiliang University. 2022. https://doi.org/10.27819/d.cnki.gzgjl.2022.000291.

Yunnan CAI. Research on cough sound recognition algorithm based on audio analysis. China Jiliang University. 2022. https://doi.org/10.27819/d.cnki.gzgjl.2022.000565.

Celesti A, et al. Leveraging audio biomarkers for enriching the tele-monitoring of patients. 2024 IEEE Symposium on Computers and Communications (ISCC), Paris, France; 2024. pp. 1–6. https://doi.org/10.1109/ISCC61673.2024.10733631.

Salamon J, Jacoby C, Bello JP. ‘A dataset and taxonomy for urban sound research’. In: Proceedings of the 22nd ACM international conference on Multimedia. 2014. pp. 1041–1044. https://doi.org/10.1145/2647868.2655045.

Dazhi E, Liu J, Zhang M, Jiang H, Mao K. RE-SMOTE: a novel imbalanced sampling method based on SMOTE with radius estimation. Comput Mater Continua. 2024;81(3):3853–80. https://doi.org/10.32604/cmc.2024.057538.

Article  Google Scholar 

Hu J, Shen L, Sun G. "Squeeze-and-Excitation Networks," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA; 2018. pp. 7132-7141. https://doi.org/10.1109/CVPR.2018.00745.

Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[J].JMLR.org, 2015. https://doi.org/10.48550/arXiv.1502.03167.