A Review on Identifying Lung Disease Sounds using different ML and DL Models

Jigisha Trivedi; Dr. Sheshang Degadwala

doi:10.32628/CSEIT24103122

Authors

Jigisha Trivedi Reserach Scholar, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India Author
Dr. Sheshang Degadwala Professor / HOD, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India Author

DOI:

https://doi.org/10.32628/CSEIT24103122

Keywords:

Machine Learning, Deep Learning, Lung Disease, Acoustic Patterns, Diagnostic Tools

Abstract

This comprehensive review explores the efficacy of various machine learning (ML) and deep learning (DL) models in identifying lung disease sounds, addressing the complex diagnostic challenges posed by the diverse acoustic patterns associated with lung diseases. ML algorithms like Support Vector Machines (SVM), Random Forests, and k-Nearest Neighbors (k-NN) offer robust classification frameworks, while DL architectures such as Convolutional Neural Networks (CNN) excel in extracting intricate audio patterns. By analyzing the performance metrics of these models, including accuracy, sensitivity, specificity, and area under the curve (AUC), the review aims to assess their comparative strengths and limitations in accurately identifying lung disease sounds. The insights gained from this review can significantly contribute to the development of more precise and effective diagnostic tools and interventions tailored to lung diseases, thus improving patient outcomes and healthcare efficiency in the realm of respiratory disorders.

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H. Gupta, D. Patel, A. Makade, K. Gupta, O. P. Vyas, and A. Puliafito, “Risk Prediction in the Life Insurance Industry Using Federated Learning Approach,” in MELECON 2022 - IEEE Mediterranean Electrotechnical Conference, Proceedings, 2022, pp. 948–953. doi: 10.1109/MELECON53508.2022.9842869. DOI: https://doi.org/10.1109/MELECON53508.2022.9842869

D. D. Pandya, A. Jadeja, S. Degadwala, and D. Vyas, “Ensemble Learning based Enzyme Family Classification using n-gram Feature,” in Proceedings - 2022 6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022, 2022, pp. 1386–1392. doi: 10.1109/ICICCS53718.2022.9788292. DOI: https://doi.org/10.1109/ICICCS53718.2022.9788292

B. Trivedi, S. Degadwala, and D. Vyas, “Parallel Data Stream Anonymization Methods: A Review,” in Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022, 2022, pp. 887–891. doi: 10.1109/ICAIS53314.2022.9743084. DOI: https://doi.org/10.1109/ICAIS53314.2022.9743084

P. Bam, S. Degadwala, R. Upadhyay, and D. Vyas, “Spoken Language Recognization Based on Features and Classification Methods: A Review,” in Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022, 2022, pp. 868–873. doi: 10.1109/ICAIS53314.2022.9743090. DOI: https://doi.org/10.1109/ICAIS53314.2022.9743090

R. Baria, S. Degadwala, R. Upadhyay, and D. Vyas, “Theoretical Evaluation of Machine And Deep Learning For Detecting Fake News,” in Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022, 2022, pp. 325–329. doi: 10.1109/ICAIS53314.2022.9742864. DOI: https://doi.org/10.1109/ICAIS53314.2022.9742864

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S. Degadwala, D. Vyas, H. Biswas, U. Chakraborty, and S. Saha, “Image Captioning Using Inception V3 Transfer Learning Model,” in Proceedings of the 6th International Conference on Communication and Electronics Systems, ICCES 2021, 2021, pp. 1103–1108. doi: 10.1109/ICCES51350.2021.9489111. DOI: https://doi.org/10.1109/ICCES51350.2021.9489111

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S. Degadwala, D. Vyas, M. R. Hossain, A. R. DIder, M. N. Ali, and P. Kuri, “Location-Based Modelling and Analysis of Threats by Using Text Mining,” Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021, pp. 1940–1944, 2021, doi: 10.1109/ICESC51422.2021.9532825. DOI: https://doi.org/10.1109/ICESC51422.2021.9532825