Screening and Diagnosis of Chronic Pharyngitis Based on Deep Learning

Zhichao Li, Jilin Huang and Zhiping Hu
Additional contact information
Zhichao Li: School of Political Science and Public Administration, East China University of Political Science and Law, Shanghai 201620, China
Jilin Huang: College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Zhiping Hu: School of Political Science and Public Administration, East China University of Political Science and Law, Shanghai 201620, China

IJERPH, 2019, vol. 16, issue 10, 1-15

Abstract: 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.

Keywords: Chronic pharyngitis; Deep learning; Convolutional neural network; Spectrogram (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/16/10/1688/pdf (application/pdf)
https://www.mdpi.com/1660-4601/16/10/1688/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:16:y:2019:i:10:p:1688-:d:230999

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().