カテゴリ: 論文誌(論文単位)グループ名: 【C】電子・情報・システム部門発行日: 2020/12/01タイトル(英語): Lung Sound Classification Using Deep Neural Networks with Pre-training―Comparison of the Performance between CNN, LSTM and Convolutional LSTM―著者名: 若本 亮佑（山口大学大学院創成科学研究科），間普 真吾（山口大学大学院創成科学研究科），木戸 尚治（大阪大学大学院医学系研究科），呉本 尭（山口大学大学院創成科学研究科）著者名(英語): Ryosuke Wakamoto (Graduate School of Sciences and Technology for Innovation, Yamaguchi University), Shingo Mabu (Graduate School of Sciences and Technology for Innovation, Yamaguchi University), Shoji Kido (Graduate School of Medicine, Osaka University), Takashi Kuremoto (Graduate School of Sciences and Technology for Innovation, Yamaguchi University)キーワード: 聴診音，深層学習，CNN，LSTM，事前学習 lung sounds，deep learning，CNN，LSTM，pre-training要約(英語): Physicians in the medical field have carried heavy burdens of diagnosis because they need to find various diseases of many patients on the basis of various examinations. Recently, to reduce their burdens, deep learning is enthusiastically applied to medical fields. For example, there have been many applications of deep learning to chest CT and X-ray images. However, there are few studies on deep learning for auscultation. Therefore, we aim to build a lung sound classification system using deep learning. Although a large number of data with annotation are generally required for deep learning, it is difficult to collect a sufficient number of lung sounds data. Therefore, we propose some lung sound classification systems with deep learning for efficiently training neural networks with a small number of data. In detail, 1) Mel-Frequency Cepstral Coefficients are used for feature extraction and 2) some pre-training techniques with three types of neural networks such as a convolutional neural network (CNN), long short term memory (LSTM), and convolutional long short term memory (C-LSTM) are designed to realize efficient learning for a small number of lung sounds data. From the experimental results, it is clarified that the proposed pre-training techniques show effective classification performance, and especially, C-LSTM with pre-training achieves higher accuracy than conventional CNN and LSTM.本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.140 No.12 （2020） 特集：電気・電子・情報関係学会東海支部連合大会本誌掲載ページ: 1402-1409 p原稿種別: 論文／日本語電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/140/12/140_1402/_article/-char/ja/
