Design and Implementation of the Traditional Chinese Medicine Constitution System Based on the Diagnosis of Tongue and Consultation

Yuan, Ye; Liao, Wei

doi:10.1109/access.2020.3047452

Cited by 12 publications

(3 citation statements)

References 29 publications

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“…These handcraft features were based on morphology of the tongue, and Gao et al 62 used the geometric features of the tongue based on the level set method derived from ACM, combined with the CNN network, and proposed a level set model with symmetry and edge constraints. However, Yuan and Liao 61 argued that tongue body and tongue coating segmentation can be clustered according to differences of the color blocks in Lab color space, and the K -means segmentation method based on Lab color space was proposed and was better than the deep learning methods of FCN, U-Net, and Deeplab-v3.…”

Section: Tongue Image Data Preprocessingmentioning

confidence: 99%

“…Sometimes combining with other healthy signals would improve the classification accuracy of syndromes. Yuan and Liao 61 combined tongue Lab color value and consultation based on a questionnaire to diagnosis constitution. They used a relationships table between tongue Lab color value and constitutions, and sentence similarity as the features to implement classification.…”

Section: Tongue Image For Syndrome Differentiationmentioning

confidence: 99%

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A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation

Liu

Peng

et al. 2023

DIGITAL HEALTH

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The rapid development of artificial intelligence technology has gradually extended from the general field to all walks of life, and intelligent tongue diagnosis is the product of a miraculous connection between this new discipline and traditional disciplines. We reviewed the deep learning methods and machine learning applied in tongue image analysis that have been studied in the last 5 years, focusing on tongue image calibration, detection, segmentation, and classification of diseases, syndromes, and symptoms/signs. Introducing technical evolutions or emerging technologies were applied in tongue image analysis; as we have noticed, attention mechanism, multiscale features, and prior knowledge were successfully applied in it, and we emphasized the value of combining deep learning with traditional methods. We also pointed out two major problems concerned with data set construction and the low reliability of performance evaluation that exist in this field based on the basic essence of tongue diagnosis in traditional Chinese medicine. Finally, a perspective on the future of intelligent tongue diagnosis was presented; we believe that the self-supervised method, multimodal information fusion, and the study of tongue pathology will have great research significance.

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Section: Tongue Image Data Preprocessingmentioning

confidence: 99%

Section: Tongue Image For Syndrome Differentiationmentioning

confidence: 99%

A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation

Liu

Peng

et al. 2023

DIGITAL HEALTH

View full text Add to dashboard Cite

show abstract

“…By observing the tongue coating, we can understand the health status of the body. The traditional tongue diagnosis [4] is made by TCM doctors according to their own knowledge level to observe the diagnosis results with the eyes. The results of the diagnosis are easily influenced by the doctor's personal level.…”

Section: Introductionmentioning

confidence: 99%

A Tongue Segmentation Algorithm Based on Deeplabv3+ Network Model

Bu¹,

Zhang²

2023

JCEIM

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When collecting tongue images in an open en- vironment with a mobile portable collection device, there will be problems of different shooting angles and unstable lighting. Due to the strong mobility of the portable acquisition device, the captured images will inevitably be blurred by jitter, which further increases the diﬀiculty of segmentation. This paper applies neural network to tongue images segmentation, and proposes a tongue images segmentation method based on deep convolutional neural network. This method is a tongue images segmentation method based on the semantic segmentation framework of DeeplabV3+. First, we modify the output category of the network. Because only the tongue region is segmented, segmentation targets can be divided into two categories when performing tongue images segmentation. One is the tongue region and the other is the background region. Then we replace the backbone network of DeeplabV3+ with a lightweight network and add an attention mechanism. Finally, we use the collected tongue images in the open environment to train the network. After the network obtains the initial segmentation result, tongue images are restored according to the same type of label, so as to obtain the required tongue images only containing tongues. The experimental results show that the method has higher segmentation accuracy for tongue images in open environment, and can better meet the needs of people for tongue images segmentation.

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Classifying Chinese Medicine Constitution Using Multimodal Deep-Learning Model

Yan

Jiang

2022

Chin. J. Integr. Med.

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Design and Implementation of the Traditional Chinese Medicine Constitution System Based on the Diagnosis of Tongue and Consultation

Cited by 12 publications

References 29 publications

A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation

A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation

A Tongue Segmentation Algorithm Based on Deeplabv3+ Network Model

Classifying Chinese Medicine Constitution Using Multimodal Deep-Learning Model

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