School of Political Science and Public Administration, East China University of Political Science and Law, Shanghai 201620, China.
College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
Int J Environ Res Public Health. 2019 May 14;16(10):1688. doi: 10.3390/ijerph16101688.
Chronic pharyngitis is a common disease, which has a long duration and a wide range of onset. It is easy to misdiagnose by mistaking it with other diseases, such as chronic tonsillitis, by using common diagnostic methods. In order to reduce costs and avoid misdiagnosis, the search for an affordable and rapid diagnostic method is becoming more and more important for chronic pharyngitis research. Speech disorder is one of the typical symptoms of patients with chronic pharyngitis. This paper introduces a convolutional neural network model for diagnosis based on the typical symptom of speech disorder. First of all, the voice data is converted into a speech spectrogram, which can better output the speech characteristic information and lay a foundation for computer diagnosis and discrimination. Second, we construct a deep convolutional neural network for the diagnosis of chronic pharyngitis through the design of the structure, the design of the network layer, and the description of the function. Finally, we perform a parameter optimization experiment on the convolutional neural network and judge the recognition efficiency of chronic pharyngitis. The results show that the convolutional neural network has a high recognition rate for patients with chronic pharyngitis and has a good diagnostic effect.
慢性咽炎是一种常见病,病程长,发病范围广。采用常规诊断方法,容易与慢性扁桃体炎等其他疾病混淆,导致误诊。为了降低成本,避免误诊,寻找一种经济实惠、快速的诊断方法,对于慢性咽炎的研究越来越重要。言语障碍是慢性咽炎患者的典型症状之一。本文基于言语障碍这一典型症状,引入一种基于卷积神经网络的诊断模型。首先,将语音数据转换为语音声谱图,这可以更好地输出语音特征信息,为计算机诊断和识别奠定基础。其次,通过结构设计、网络层设计和功能描述,为慢性咽炎的诊断构建一个深度卷积神经网络。最后,对卷积神经网络进行参数优化实验,并判断慢性咽炎的识别效率。结果表明,该卷积神经网络对慢性咽炎患者的识别率较高,具有较好的诊断效果。