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Updated: May 24, 2025

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Chaos-MLP: Chaotic Transform MLP-Like Architecture for Medical Images Multi-Label Recognition Task.

Mengjian Zhang, Guihua Wen, Pei Yang

    IEEE Journal of Biomedical and Health Informatics
    |March 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Automated body constitution recognition (BCR) in Traditional Chinese Medicine (TCM) is enhanced by the novel Chaos-MLP model. This method improves disease prevention and diagnosis accuracy using new datasets and a unique loss function.

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    Area of Science:

    • Artificial Intelligence
    • Traditional Chinese Medicine
    • Medical Image Analysis

    Background:

    • The theory of "three-stage prevention" is central to modern Chinese medicine for disease prevention.
    • Automated body constitution recognition (BCR) is crucial for intelligent Traditional Chinese Medicine (TCM) systems, aiding disease prevention and diagnosis.
    • BCR presents a complex multi-label recognition challenge due to the composite nature of TCM constitution theory.

    Purpose of the Study:

    • To develop an advanced automated body constitution recognition (BCR) system for Traditional Chinese Medicine (TCM).
    • To introduce a novel MLP-like architecture, Chaos-MLP, and a Binary Center Cognitive Gravity Loss (BCCGL) for improved BCR performance.
    • To establish new benchmark datasets for multi-label facial and tongue body constitution recognition.

    Main Methods:

    • Construction of two novel multi-label datasets: Multi-label Facial Body Constitution (MFBC) and Multi-label Tongue Body Constitution (MTBC).
    • Design of Chaos-MLP, an MLP-like neural network architecture that utilizes chaotic transforms to enhance feature distinguishability from medical images.
    • Integration of channel chaotic features with directional features and implementation of BCCGL to address label imbalance in BCR.

    Main Results:

    • The proposed Chaos-MLP model demonstrated superior performance on both MFBC and MTBC datasets compared to existing state-of-the-art (SOTA) methods.
    • Chaos-MLP outperformed other MLP-like networks (Wave-MLP, Cycle-MLP, Vip, Active-MLP) and a vision graph-based neural network (VGNN).
    • The chaotic transform effectively enhanced feature distinctiveness, and BCCGL improved learning with unbalanced constitution labels.

    Conclusions:

    • The Chaos-MLP model, combined with BCCGL, represents a significant advancement in automated body constitution recognition for intelligent TCM.
    • The developed datasets and methodology provide a strong foundation for future research in TCM-based disease prevention and diagnosis.
    • This work highlights the potential of integrating advanced deep learning techniques with traditional medical theories for enhanced healthcare outcomes.