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Screening and Diagnosis of Chronic Pharyngitis Based on Deep Learning

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zhichao Li & Jilin Huang & Zhiping Hu, 2019. "Screening and Diagnosis of Chronic Pharyngitis Based on Deep Learning," IJERPH, MDPI, vol. 16(10), pages 1-15, May.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:10:p:1688-:d:230999
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    References listed on IDEAS

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    1. Zhichao Li & Jilin Huang, 2019. "How to Mitigate Traffic Congestion Based on Improved Ant Colony Algorithm: A Case Study of a Congested Old Area of a Metropolis," Sustainability, MDPI, vol. 11(4), pages 1-15, February.
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