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Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds
In recent years deep learning models improve the diagnosis performance of many diseases especially respiratory diseases. This paper will propose an evaluation for the performance of different deep learning models associated with the raw lung auscultation sounds in detecting respiratory pathologies t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510581/ https://www.ncbi.nlm.nih.gov/pubmed/36186666 http://dx.doi.org/10.1007/s00500-022-07499-6 |
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author | Alqudah, Ali Mohammad Qazan, Shoroq Obeidat, Yusra M. |
author_facet | Alqudah, Ali Mohammad Qazan, Shoroq Obeidat, Yusra M. |
author_sort | Alqudah, Ali Mohammad |
collection | PubMed |
description | In recent years deep learning models improve the diagnosis performance of many diseases especially respiratory diseases. This paper will propose an evaluation for the performance of different deep learning models associated with the raw lung auscultation sounds in detecting respiratory pathologies to help in providing diagnostic of respiratory pathologies in digital recorded respiratory sounds. Also, we will find out the best deep learning model for this task. In this paper, three different deep learning models have been evaluated on non-augmented and augmented datasets, where two different datasets have been utilized to generate four different sub-datasets. The results show that all the proposed deep learning methods were successful and achieved high performance in classifying the raw lung sounds, the methods were applied on different datasets and used either augmentation or non-augmentation. Among all proposed deep learning models, the CNN–LSTM model was the best model in all datasets for both augmentation and non-augmentation cases. The accuracy of CNN–LSTM model using non-augmentation was 99.6%, 99.8%, 82.4%, and 99.4% for datasets 1, 2, 3, and 4, respectively, and using augmentation was 100%, 99.8%, 98.0%, and 99.5% for datasets 1, 2, 3, and 4, respectively. While the augmentation process successfully helps the deep learning models in enhancing their performance on the testing datasets with a notable value. Moreover, the hybrid model that combines both CNN and LSTM techniques performed better than models that are based only on one of these techniques, this mainly refers to the use of CNN for automatic deep features extraction from lung sound while LSTM is used for classification. |
format | Online Article Text |
id | pubmed-9510581 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-95105812022-09-26 Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds Alqudah, Ali Mohammad Qazan, Shoroq Obeidat, Yusra M. Soft comput Data Analytics and Machine Learning In recent years deep learning models improve the diagnosis performance of many diseases especially respiratory diseases. This paper will propose an evaluation for the performance of different deep learning models associated with the raw lung auscultation sounds in detecting respiratory pathologies to help in providing diagnostic of respiratory pathologies in digital recorded respiratory sounds. Also, we will find out the best deep learning model for this task. In this paper, three different deep learning models have been evaluated on non-augmented and augmented datasets, where two different datasets have been utilized to generate four different sub-datasets. The results show that all the proposed deep learning methods were successful and achieved high performance in classifying the raw lung sounds, the methods were applied on different datasets and used either augmentation or non-augmentation. Among all proposed deep learning models, the CNN–LSTM model was the best model in all datasets for both augmentation and non-augmentation cases. The accuracy of CNN–LSTM model using non-augmentation was 99.6%, 99.8%, 82.4%, and 99.4% for datasets 1, 2, 3, and 4, respectively, and using augmentation was 100%, 99.8%, 98.0%, and 99.5% for datasets 1, 2, 3, and 4, respectively. While the augmentation process successfully helps the deep learning models in enhancing their performance on the testing datasets with a notable value. Moreover, the hybrid model that combines both CNN and LSTM techniques performed better than models that are based only on one of these techniques, this mainly refers to the use of CNN for automatic deep features extraction from lung sound while LSTM is used for classification. Springer Berlin Heidelberg 2022-09-26 2022 /pmc/articles/PMC9510581/ /pubmed/36186666 http://dx.doi.org/10.1007/s00500-022-07499-6 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Data Analytics and Machine Learning Alqudah, Ali Mohammad Qazan, Shoroq Obeidat, Yusra M. Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds |
title | Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds |
title_full | Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds |
title_fullStr | Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds |
title_full_unstemmed | Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds |
title_short | Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds |
title_sort | deep learning models for detecting respiratory pathologies from raw lung auscultation sounds |
topic | Data Analytics and Machine Learning |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510581/ https://www.ncbi.nlm.nih.gov/pubmed/36186666 http://dx.doi.org/10.1007/s00500-022-07499-6 |
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