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An Intelligent Tongue Diagnosis System via Deep Learning on the Android Platform.
Yang, Zibin; Zhao, Yuping; Yu, Jiarui; Mao, Xiaobo; Xu, Huaxing; Huang, Luqi.
Afiliación
  • Yang Z; School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
  • Zhao Y; China Academy of Chinese Medical Sciences, Beijing 100020, China.
  • Yu J; School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
  • Mao X; School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
  • Xu H; School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
  • Huang L; China Academy of Chinese Medical Sciences, Beijing 100020, China.
Diagnostics (Basel) ; 12(10)2022 Oct 10.
Article en En | MEDLINE | ID: mdl-36292140
To quickly and accurately identify the pathological features of the tongue, we developed an intelligent tongue diagnosis system that uses deep learning on a mobile terminal. We also propose an efficient and accurate tongue image processing algorithm framework to infer the category of the tongue. First, a software system integrating registration, login, account management, tongue image recognition, and doctor-patient dialogue was developed based on the Android platform. Then, the deep learning models, based on the official benchmark models, were trained by using the tongue image datasets. The tongue diagnosis algorithm framework includes the YOLOv5s6, U-Net, and MobileNetV3 networks, which are employed for tongue recognition, tongue region segmentation, and tongue feature classification (tooth marks, spots, and fissures), respectively. The experimental results demonstrate that the performance of the tongue diagnosis model was satisfying, and the accuracy of the final classification of tooth marks, spots, and fissures was 93.33%, 89.60%, and 97.67%, respectively. The construction of this system has a certain reference value for the objectification and intelligence of tongue diagnosis.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Diagnostics (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Diagnostics (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza