The Research of Tongue Features Base on Deep Learning

Corresponding author: KOU De-shuang, 294107865@qq.com

DOI: 10.12201/bmr.202404.00020

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  Abstract: Purpose/Significance The present research utilizes deep learning methodologies within the context of tongue manifestation analysis, with the objective of automating the process and contributing to the establishment of standardized and objective benchmarks for tongue diagnosis in Traditional Chinese Medicine (TCM). This work endeavors to significantly propel the progression towards the modernization of TCM diagnostic procedures. Method/Process Firstly, a new semantic segmentation loss function has been devised that fundamentally integrates regional association and label relaxation techniques. This advanced function imposes a constraint on the segmentation model such that it learns to account for the inter-pixel relationships within specific regions of the tongue images, while simultaneously endowing the model with a degree of resilience against inaccurately annotated labels. Secondly, leveraging the latent color-related priors inherently present within the characteristics of tongue images, a strategic decision was made to decompose these features exclusively into two separate streams for subsequent multi-label classification tasks during the models architectural design stage. This strategic maneuver serves to expedite the models convergence process while significantly diminishing its overall complexity. Result/Conclusion Lastly, the effectiveness of the algorithm proposed in this paper was validated through experimentation on our custom-built dataset. The results showed an impressive 96.57% Mean Intersection over Union (MIoU) score for tongue image segmentation, along with macro F1-score and average accuracy values of 88.58% and 82.59%, respectively.

  Key words: Transfer Learning; Tongue Image Features; Deep Learning; Tongue Segmentation; Tongue Diagnosis of Traditional Chinese Medicine

  Submit time: 11 April 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2024-02-19	bmr.202404.00020V1	Download

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