Automatic auscultation classification of abnormal lung sounds in critical patients through deep learning models

Yu Sheng Wu, Chia Hung Liao, Shyan Ming Yuan*

*此作品的通信作者

研究成果: Conference contribution同行評審

5 引文 斯高帕斯(Scopus)

摘要

This research aims to use the output signals of a stethoscope and classify them through deep learning models automatically. In this research, the dataset consists of four classes, normal, wheezing, crackles, and unknown are used. To effectively classify each signal, we use the spectrogram generated by the short-time fast Fourier transform as the feature value of each lung sound signal and found the best parameters to do model selection. Besides, we also adopt Depthwise separable (DS) convolution technic, and refer to the architecture of Mobile-Net, to achieve the purpose of high accuracy and low model parameters.

原文English
主出版物標題Proceedings of the 3rd IEEE International Conference on Knowledge Innovation and Invention 2020, ICKII 2020
編輯Teen-Hang Meen
發行者Institute of Electrical and Electronics Engineers Inc.
頁面9-11
頁數3
ISBN(電子)9781728193335
DOIs
出版狀態Published - 21 8月 2020
事件3rd IEEE International Conference on Knowledge Innovation and Invention, ICKII 2020 - Kaohsiung, 台灣
持續時間: 21 8月 202023 8月 2020

出版系列

名字Proceedings of the 3rd IEEE International Conference on Knowledge Innovation and Invention 2020, ICKII 2020

Conference

Conference3rd IEEE International Conference on Knowledge Innovation and Invention, ICKII 2020
國家/地區台灣
城市Kaohsiung
期間21/08/2023/08/20

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