Parallel Network Model of Abnormal Respiratory Sound Classification with Stacking Ensemble (스태킹 앙상블을 이용한 병렬 네트워크 이상호흡음 분류 모델)

Nam Myungwoo (남명우); Young-Jin Choi (최영진); Hoe-Ryeon Choi (최회련); LEE, Hong Chul (이홍철)

doi:10.9708/jksci.2021.26.11.021

Parallel Network Model of Abnormal Respiratory Sound Classification with Stacking Ensemble

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2021, 26(11), pp.21-31
DOI : 10.9708/jksci.2021.26.11.021
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : September 15, 2021
Accepted : November 11, 2021
Published : November 30, 2021

Nam Myungwoo ¹, Young-Jin Choi ¹, Hoe-Ryeon Choi ¹, LEE, Hong Chul ¹

¹고려대학교

Accredited

ABSTRACT

As the COVID-19 pandemic rapidly changes healthcare around the globe, the need for smart healthcare that allows for remote diagnosis is increasing. The current classification of respiratory diseases cost high and requires a face-to-face visit with a skilled medical professional, thus the pandemic significantly hinders monitoring and early diagnosis. Therefore, the ability to accurately classify and diagnose respiratory sound using deep learning-based AI　models is essential to modern medicine as a remote alternative to the current stethoscope. In this study, we propose a deep learning-based respiratory sound classification model using data collected from medical experts. The sound data were preprocessed with BandPassFilter, and the relevant respiratory audio features were extracted with Log-Mel Spectrogram and Mel Frequency Cepstral Coefficient (MFCC). Subsequently, a Parallel CNN network model was trained on these two inputs using stacking ensemble techniques combined with various machine learning classifiers to efficiently classify and detect abnormal respiratory sounds with high accuracy. The model proposed in this paper classified abnormal respiratory sounds with an accuracy of 96.9%, which is approximately 6.1% higher than the classification accuracy of baseline model.

KEYWORDS

Respiratory Sound Classification, Wheezes, Crackles, Convolutional Neural Network(CNN), Stacking Ensemble

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* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Parallel Network Model of Abnormal Respiratory Sound Classification with Stacking Ensemble

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* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Parallel Network Model of Abnormal Respiratory Sound Classification with Stacking Ensemble

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* References for papers published after 2023 are currently being built.