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A Review of Classification Techniques Based on Neural Networks for Pulmonary Obstructive Diseases 基于神经网络的肺阻塞性疾病分类技术综述
Pub Date : 2019-04-01 DOI: 10.2139/ssrn.3363485
Rupesh Dubey, Rajesh M. Bodade
The automatic analysis of respiratory sound is of enormous challenge. The success of the Automatic Classification of pulmonary obstructive diseases can carry a radical change in the medical field. The extracted features of adventitious respiratory sound are classified; the ability of classifier provides the best results. Neural Networks have come out to be a promising classifier for respiratory disorders. The paper reviews works of classification of adventitious and vascular sounds by methods like Neural Networks, KNN, ANN, SNN, CNN, and others. CNN technique recently applied to classification of respiratory sound is analyzed. Due to a large number of ongoing researches in the field of classification of respiratory diseases by signal processing techniques, there is a great need to summaries results at a place. It is concluded that CNN has emerged out as one of the better technique for the classification of respiratory sounds.
呼吸声的自动分析是一个巨大的挑战。《肺阻塞性疾病自动分类》的成功可以在医学领域带来根本性的变化。对提取的不定音特征进行分类;分类器的能力提供了最好的结果。神经网络已被证明是一种很有前途的呼吸系统疾病分类器。本文综述了利用神经网络、KNN、ANN、SNN、CNN等方法对不定音和血管音进行分类的工作。分析了近年来CNN技术在呼吸音分类中的应用。由于信号处理技术在呼吸系统疾病分类领域的大量研究正在进行中,因此非常需要对研究结果进行汇总。由此得出结论,CNN已成为较好的呼吸音分类技术之一。
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引用次数: 10
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MedRN: COPD (Topic)
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