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Related Experiment Video

Updated: Jan 10, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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A Deep Regression Model for Tongue Image Color Correction Based on CNN.

Xiyuan Cao1, Delong Zhang1, Chunyang Jin1

  • 1State Key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instrument, North University of China, Taiyuan 030051, China.

Journal of Imaging
|November 26, 2025
PubMed
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TococoNet, a novel convolutional neural network, effectively corrects color bias in tongue images. This deep learning model enhances diagnostic accuracy by restoring true colors in medical imaging.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Image color authenticity is compromised by varying viewing conditions, leading to visual inconsistencies.
  • Deep learning offers advanced solutions for image processing and optimization challenges.

Purpose of the Study:

  • To introduce TococoNet (Tongue Color Correction Network), a novel regression model for eliminating color bias in tongue images.
  • To evaluate TococoNet's effectiveness in color correction for diagnostic applications.

Main Methods:

  • Developed a convolutional neural network (CNN) model, TococoNet, featuring symmetric encoder-decoder U-Blocks and M-Block for feature fusion.
  • Trained the model using simulated common biased colors and conducted correction experiments with random color biases.
Keywords:
CNNscolor correctiondeep learningtongue features

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  • Utilized image quality indicators and color distance (ΔE) to assess correction performance.

    • Main Results:

    • TococoNet demonstrated superior color correction compared to conventional algorithms and shallow networks.
    • Achieved up to 84% correction effectiveness (ΔE) for tongue images with random color casts.
    • Significantly reduced maximum ΔE from 30.38 to 6.05 in actual captured images.

    Conclusions:

    • TococoNet exhibits excellent color correction capabilities for tongue images.
    • The model shows promising potential for clinical assistance and automatic tongue diagnosis.