Acoustic-based deep learning architectures for lung disease diagnosis: a comprehensive overview
Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient’s respiratory role. Modern technological progress has guided the g...
| Main Authors: | , , , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/106411/ |
| _version_ | 1848864760111562752 |
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| author | Sfayyih, Alyaa Hamel Sabry, Ahmad H. Mohammed Jameel, Shymaa Sulaiman, Nasri Raafat, Safanah Mudheher Humaidi, Amjad J. Al Kubaiaisi, Yasir Mahmood |
| author_facet | Sfayyih, Alyaa Hamel Sabry, Ahmad H. Mohammed Jameel, Shymaa Sulaiman, Nasri Raafat, Safanah Mudheher Humaidi, Amjad J. Al Kubaiaisi, Yasir Mahmood |
| author_sort | Sfayyih, Alyaa Hamel |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient’s respiratory role. Modern technological progress has guided the growth of computer-based respiratory speech investigation, a valuable tool for detecting lung abnormalities and diseases. Several recent studies have reviewed this important area, but none are specific to lung sound-based analysis with deep-learning architectures from one side and the provided information was not sufficient for a good understanding of these techniques. This paper gives a complete review of prior deep-learning-based architecture lung sound analysis. Deep-learning-based respiratory sound analysis articles are found in different databases including the Plos, ACM Digital Libraries, Elsevier, PubMed, MDPI, Springer, and IEEE. More than 160 publications were extracted and submitted for assessment. This paper discusses different trends in pathology/lung sound, the common features for classifying lung sounds, several considered datasets, classification methods, signal processing techniques, and some statistical information based on previous study findings. Finally, the assessment concludes with a discussion of potential future improvements and recommendations. |
| first_indexed | 2025-11-15T13:53:56Z |
| format | Article |
| id | upm-106411 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:53:56Z |
| publishDate | 2023 |
| publisher | Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1064112024-08-16T08:15:25Z http://psasir.upm.edu.my/id/eprint/106411/ Acoustic-based deep learning architectures for lung disease diagnosis: a comprehensive overview Sfayyih, Alyaa Hamel Sabry, Ahmad H. Mohammed Jameel, Shymaa Sulaiman, Nasri Raafat, Safanah Mudheher Humaidi, Amjad J. Al Kubaiaisi, Yasir Mahmood Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient’s respiratory role. Modern technological progress has guided the growth of computer-based respiratory speech investigation, a valuable tool for detecting lung abnormalities and diseases. Several recent studies have reviewed this important area, but none are specific to lung sound-based analysis with deep-learning architectures from one side and the provided information was not sufficient for a good understanding of these techniques. This paper gives a complete review of prior deep-learning-based architecture lung sound analysis. Deep-learning-based respiratory sound analysis articles are found in different databases including the Plos, ACM Digital Libraries, Elsevier, PubMed, MDPI, Springer, and IEEE. More than 160 publications were extracted and submitted for assessment. This paper discusses different trends in pathology/lung sound, the common features for classifying lung sounds, several considered datasets, classification methods, signal processing techniques, and some statistical information based on previous study findings. Finally, the assessment concludes with a discussion of potential future improvements and recommendations. Multidisciplinary Digital Publishing Institute 2023-05-16 Article PeerReviewed Sfayyih, Alyaa Hamel and Sabry, Ahmad H. and Mohammed Jameel, Shymaa and Sulaiman, Nasri and Raafat, Safanah Mudheher and Humaidi, Amjad J. and Al Kubaiaisi, Yasir Mahmood (2023) Acoustic-based deep learning architectures for lung disease diagnosis: a comprehensive overview. Diagnostics, 13 (10). art. no. 1748. pp. 1-24. ISSN 2075-4418 https://www.mdpi.com/2075-4418/13/10/1748 10.3390/diagnostics13101748 |
| spellingShingle | Sfayyih, Alyaa Hamel Sabry, Ahmad H. Mohammed Jameel, Shymaa Sulaiman, Nasri Raafat, Safanah Mudheher Humaidi, Amjad J. Al Kubaiaisi, Yasir Mahmood Acoustic-based deep learning architectures for lung disease diagnosis: a comprehensive overview |
| title | Acoustic-based deep learning architectures for lung disease diagnosis: a comprehensive overview |
| title_full | Acoustic-based deep learning architectures for lung disease diagnosis: a comprehensive overview |
| title_fullStr | Acoustic-based deep learning architectures for lung disease diagnosis: a comprehensive overview |
| title_full_unstemmed | Acoustic-based deep learning architectures for lung disease diagnosis: a comprehensive overview |
| title_short | Acoustic-based deep learning architectures for lung disease diagnosis: a comprehensive overview |
| title_sort | acoustic-based deep learning architectures for lung disease diagnosis: a comprehensive overview |
| url | http://psasir.upm.edu.my/id/eprint/106411/ http://psasir.upm.edu.my/id/eprint/106411/ http://psasir.upm.edu.my/id/eprint/106411/ |