听诊
计算机科学
呼吸音
卷积神经网络
听诊器
分类器(UML)
人工智能
语音识别
深度学习
感知器
机器学习
重症监护医学
医学
人工神经网络
哮喘
放射科
内科学
作者
Tran Minh Quan,Kirill Lipatov,Hsin‐Yi Wang,Brian Pickering,Vitaly Herasevich
标识
DOI:10.1109/embc46164.2021.9630294
摘要
Management of respiratory conditions relies on timely diagnosis and institution of appropriate management. Computerized analysis and classification of breath sounds has a potential to enhance reliability and accuracy of diagnostic modality while making it suitable for remote monitoring, personalized uses, and self-management uses. In this paper, we describe and compare sound recognition models aimed at automatic diagnostic differentiation of healthy persons vs patients with COPD vs patients with pneumonia using deep learning approaches such as Multi-layer Perceptron Classifier (MLPClassifier) and Convolutional Neural Networks (CNN).Clinical Relevance–Healthcare providers and researchers interested in the field of medical sound analysis, specifically automatic detection/classification of auscultation sound and early diagnosis of respiratory conditions may benefit from this paper.
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