An Effective Lung Sound Classification System for Respiratory Disease Diagnosis Using DenseNet CNN Model with Sound Pre-processing Engine

Wei Bang Ma; Xiang Yuan Deng; Yang Yang; Wai Chi Fang

doi:10.1109/BioCAS54905.2022.9948568

An Effective Lung Sound Classification System for Respiratory Disease Diagnosis Using DenseNet CNN Model with Sound Pre-processing Engine

Wei Bang Ma, Xiang Yuan Deng, Yang Yang, Wai Chi Fang^*

^*Corresponding author for this work

Institute of Electronics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

Lung sound auscultation is a simple, inexpensive, and non-invasive method of diagnosing respiratory diseases. But the experience of each physician may be different, resulting in inconsistent diagnostic results. To solve this problem, we built a deep learning model for classifying lung sounds, which can provide physicians with a more consistent reference for accurate diagnosis. Based on lung sound dataset obtained on children aged from 1 month to 18 years old, we proposed a classification system with optimized pre-processing methods combined with a DenseNet169 CNN model. Four different classification tasks results are provided with respect to a total score given rule, 89.0% for task 1.1, 90.9% for task 1.2, 83.8% for task 2.1 and 67.3% for task 2.2.

Original language	English
Title of host publication	BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference
Subtitle of host publication	Intelligent Biomedical Systems for a Better Future, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	218-222
Number of pages	5
ISBN (Electronic)	9781665469173
DOIs	https://doi.org/10.1109/BioCAS54905.2022.9948568
State	Published - 2022
Event	2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022 - Taipei, Taiwan Duration: 13 Oct 2022 → 15 Oct 2022

Publication series

Name	BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings

Conference

Conference	2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022
Country/Territory	Taiwan
City	Taipei
Period	13/10/22 → 15/10/22

Keywords

artificial intelligence
convolution neural network
lung sound
respiratory disease

Access to Document

10.1109/BioCAS54905.2022.9948568

Cite this

Ma, W. B., Deng, X. Y., Yang, Y., & Fang, W. C. (2022). An Effective Lung Sound Classification System for Respiratory Disease Diagnosis Using DenseNet CNN Model with Sound Pre-processing Engine. In BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings (pp. 218-222). (BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BioCAS54905.2022.9948568

Ma, Wei Bang ; Deng, Xiang Yuan ; Yang, Yang et al. / An Effective Lung Sound Classification System for Respiratory Disease Diagnosis Using DenseNet CNN Model with Sound Pre-processing Engine. BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 218-222 (BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings).

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title = "An Effective Lung Sound Classification System for Respiratory Disease Diagnosis Using DenseNet CNN Model with Sound Pre-processing Engine",

abstract = "Lung sound auscultation is a simple, inexpensive, and non-invasive method of diagnosing respiratory diseases. But the experience of each physician may be different, resulting in inconsistent diagnostic results. To solve this problem, we built a deep learning model for classifying lung sounds, which can provide physicians with a more consistent reference for accurate diagnosis. Based on lung sound dataset obtained on children aged from 1 month to 18 years old, we proposed a classification system with optimized pre-processing methods combined with a DenseNet169 CNN model. Four different classification tasks results are provided with respect to a total score given rule, 89.0% for task 1.1, 90.9% for task 1.2, 83.8% for task 2.1 and 67.3% for task 2.2.",

keywords = "artificial intelligence, convolution neural network, lung sound, respiratory disease",

author = "Ma, {Wei Bang} and Deng, {Xiang Yuan} and Yang Yang and Fang, {Wai Chi}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022 ; Conference date: 13-10-2022 Through 15-10-2022",

year = "2022",

doi = "10.1109/BioCAS54905.2022.9948568",

language = "English",

series = "BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "218--222",

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address = "美國",

}

Ma, WB, Deng, XY, Yang, Y & Fang, WC 2022, An Effective Lung Sound Classification System for Respiratory Disease Diagnosis Using DenseNet CNN Model with Sound Pre-processing Engine. in BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings. BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 218-222, 2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022, Taipei, Taiwan, 13/10/22. https://doi.org/10.1109/BioCAS54905.2022.9948568

An Effective Lung Sound Classification System for Respiratory Disease Diagnosis Using DenseNet CNN Model with Sound Pre-processing Engine. / Ma, Wei Bang; Deng, Xiang Yuan; Yang, Yang et al.
BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 218-222 (BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Effective Lung Sound Classification System for Respiratory Disease Diagnosis Using DenseNet CNN Model with Sound Pre-processing Engine

AU - Ma, Wei Bang

AU - Deng, Xiang Yuan

AU - Yang, Yang

AU - Fang, Wai Chi

PY - 2022

Y1 - 2022

N2 - Lung sound auscultation is a simple, inexpensive, and non-invasive method of diagnosing respiratory diseases. But the experience of each physician may be different, resulting in inconsistent diagnostic results. To solve this problem, we built a deep learning model for classifying lung sounds, which can provide physicians with a more consistent reference for accurate diagnosis. Based on lung sound dataset obtained on children aged from 1 month to 18 years old, we proposed a classification system with optimized pre-processing methods combined with a DenseNet169 CNN model. Four different classification tasks results are provided with respect to a total score given rule, 89.0% for task 1.1, 90.9% for task 1.2, 83.8% for task 2.1 and 67.3% for task 2.2.

AB - Lung sound auscultation is a simple, inexpensive, and non-invasive method of diagnosing respiratory diseases. But the experience of each physician may be different, resulting in inconsistent diagnostic results. To solve this problem, we built a deep learning model for classifying lung sounds, which can provide physicians with a more consistent reference for accurate diagnosis. Based on lung sound dataset obtained on children aged from 1 month to 18 years old, we proposed a classification system with optimized pre-processing methods combined with a DenseNet169 CNN model. Four different classification tasks results are provided with respect to a total score given rule, 89.0% for task 1.1, 90.9% for task 1.2, 83.8% for task 2.1 and 67.3% for task 2.2.

KW - artificial intelligence

KW - convolution neural network

KW - lung sound

KW - respiratory disease

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DO - 10.1109/BioCAS54905.2022.9948568

M3 - Conference contribution

AN - SCOPUS:85142937600

T3 - BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings

SP - 218

EP - 222

BT - BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference

PB - Institute of Electrical and Electronics Engineers Inc.

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Ma WB, Deng XY, Yang Y, Fang WC. An Effective Lung Sound Classification System for Respiratory Disease Diagnosis Using DenseNet CNN Model with Sound Pre-processing Engine. In BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 218-222. (BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings). doi: 10.1109/BioCAS54905.2022.9948568

An Effective Lung Sound Classification System for Respiratory Disease Diagnosis Using DenseNet CNN Model with Sound Pre-processing Engine

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