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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 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 languageEnglish
Title of host publicationBioCAS 2022 - IEEE Biomedical Circuits and Systems Conference
Subtitle of host publicationIntelligent Biomedical Systems for a Better Future, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages218-222
Number of pages5
ISBN (Electronic)9781665469173
DOIs
StatePublished - 2022
Event2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022 - Taipei, Taiwan
Duration: 13 Oct 202215 Oct 2022

Publication series

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

Conference

Conference2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022
Country/TerritoryTaiwan
CityTaipei
Period13/10/2215/10/22

Keywords

  • artificial intelligence
  • convolution neural network
  • lung sound
  • respiratory disease

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