2026-03-14T23:00:51Z https://kyutech.repo.nii.ac.jp/oai

oai:kyutech.repo.nii.ac.jp:00007863 2025-06-05T07:45:27Z 8:9

scSE-CRNNと3種類の呼吸音変換画像による呼吸音の分類 Classification of Respiratory Sounds by scSE-CRNN from Triple Types of Respiratory Sound Images 浅谷, 尚希 Asatani, Naoki 40742466 陸, 慧敏 Lu, Huimin リク, ケイビン 80295005 Kamiya, Tohru 神谷, 亨 金, 亨燮 Kim, Hyoungseop 間普, 真吾 Mabu, Shingo 木戸, 尚治 Kido, Shoji Copyright (c) 2021 医用画像情報学会 492 Respiratory Sounds Classification Computer Aided Diagnosis Time-Frequency Analysis Deep Learning Due to the respiratory diseases such as chronic obstructive pulmonary disease and lower respiratory tract infections nearly 8 million people were died worldwide each year. Reducing the number of deaths from respiratory diseases is a challenge to be solved worldwide. Early detection is the most efficient way to reduce the number of deaths in respiratory illness. As a result, the spread of infection can be suppressed, and the therapeutic effect can be enhanced. Currently, auscultation is performed as a promising method for early detection of respiratory diseases. Auscultation can estimate respiratory diseases by distinguishing abnormal sounds contained in respiratory sounds. However, medical staff need to be trained to perform auscultation with high accuracy. Also, the diagnostic results depend on each staff subjectively, which can lead to inconsistent results. Therefore, in some environments, a shortage of specialized health care workers can lead to the spread of respiratory illness. To solve this problem, an application that analyzes respiratory sounds and outputs diagnostic results is needed. In this paper, we use a newly proposed deep learning model to automatically classify the respiratory sound data from the ICBHI 2017 Challenge Dataset. Short-Time Fourier Transform, Constant-Q Transform, and Continuous Wavelet Transform are applied to the respiratory sound data to convert it into the time-frequency region. Then, the obtained three types of breath sound images are input to CRNN (Convolutional Recurrent Neural Network) having scSE (Spatial and Channel Squeeze & Excitation) Block. The accuracy is improved by weighting the features of each image. As a result, AUC (Area Under the Curve): (Normal:0.87, Crackle:0.88, Wheeze:0.92, Both:0.89), Sensitivity: 0.67, Specificity: 0.82, Average Score: 0.75, Harmonic Score: 0.74, Accuracy: 0.75 were obtained. 医用画像情報学会 2021-12-27 jpn journal article VoR http://hdl.handle.net/10228/00009066 https://kyutech.repo.nii.ac.jp/records/7863 https://cir.nii.ac.jp/crid/1390290537431570688 https://doi.org/10.11318/mii.38.152 AN10156808 1880-4977 0910-1543 医用画像情報学会雑誌 Medical Imaging and Information Sciences 38 4 152 159 https://kyutech.repo.nii.ac.jp/record/7863/files/LaSEINE-2021_032.pdf application/pdf 2.3 MB 2023-02-02