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Related Experiment Video

Updated: Nov 11, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

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Deep sparse transfer learning for remote smart tongue diagnosis.

Xu Zhang1, Wei Huang2, Jing Gao1

  • 1The School of Software Technology, Dalian University of Technology, Dalian 116620, China.

Mathematical Biosciences and Engineering : MBE
|March 24, 2021
PubMed
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This study introduces a compact deep transfer learning model for smart tongue diagnosis on edge devices. It overcomes limited data and computational needs, enabling efficient remote diagnostics.

Area of Science:

  • Artificial Intelligence
  • Medical Diagnostics
  • Edge Computing

Background:

  • Deploying deep learning for immediate processing faces challenges like large neural network size and limited training data.
  • High computing and low latency are crucial for remote smart tongue diagnosis using edge computing.
  • Modeling intrinsic diagnosis patterns with scarce clinical data is a significant hurdle.

Purpose of the Study:

  • To propose an efficient and compact deep neural network for smart tongue diagnosis on edge devices.
  • To address the challenges of limited training data and high computational demands in remote diagnostics.
  • To enable widespread deployment of smart tongue diagnosis on low-performance infrastructures.

Main Methods:

  • A deep transfer learning model is developed utilizing a similar-sparse domain adaptation (SSDA) scheme.
Keywords:
deep transfer learningdomain adaptationpruningremote diagnosissparse networktraditional Chinese medicine

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  • A transfer strategy of similar data is employed to efficiently transfer knowledge, compensating for insufficient clinical tongue images.
  • Network pruning is applied to simplify the structure while retaining transferability for domain adaptation, resulting in a compact model with two sparse networks.

    • Main Results:

    • The proposed model achieves competitive results using fewer training data samples and parameters.
    • The model demonstrates reduced power and memory consumption.
    • The developed compact model is suitable for deployment on resource-constrained edge devices.

    Conclusions:

    • The deep transfer learning model effectively addresses the limitations of data scarcity and computational requirements for smart tongue diagnosis.
    • The SSDA scheme facilitates efficient knowledge transfer and model compression.
    • The research enables the practical implementation of smart tongue diagnosis on low-performance edge computing infrastructures.