Natural tongue physique identification using hybrid deep learning methods

Li, Huihui; Wen, Gongjian; Zeng, Haibin

doi:10.1007/s11042-018-6279-8

“…The results proved that the canny algorithm generates a large number of pseudo-edges after edge cutting, while the chan vese method can automatically select the best edge information and has a greater clinical value than the canny algorithm. Li et al 107 proposed to use of calibrated neural networks mixed with other deep learning methods for improving the accuracy of tongue detection and changing the accuracy of tongue recognition taken under natural conditions. Li et al 108 proposed to capture tongue images using hyperspectral tongues and then use hidden Markov models to classify tongue fissures into 12 classes, and the method showed good performance in tongue classification.…”

Section: Research and Progress Of Image Processing Algorithmsmentioning

confidence: 99%

Research and application of tongue and face diagnosis based on deep learning

Li

¹

,

Huang

²

,

Zhong

³

et al. 2022

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Objective To explore the technical research and application characteristics of deep learning in tongue-facial diagnosis. Methods Through summarizing the merits and demerits of current image processing techniques used in the traditional medical tongue and face diagnosis, the research status of deep learning in tongue image preprocessing, segmentation, and classification was analyzed and reviewed, and the algorithm was compared and verified with the real tongue and face image. Images of the face and tongue used for diagnosis in conventional medicine were systematically reviewed, from acquisition and pre-processing to segmentation, classification, algorithm comparison, result from analysis, and application. Results Deep learning improved the speed and accuracy of tongue and face diagnostic image data processing. Among them, the average intersection ratio of U-net and Seg-net models exceeded 0.98, and the segmentation speed ranged from 54 to 58 ms. Conclusion There is no unified standard for lingual-facial diagnosis objectification in terms of image acquisition conditions and image processing methods, thus further research is indispensable. It is feasible to use the images acquired by mobile in the field of medical image analysis by reducing the influence of environmental and other factors on the quality of lingual-facial diagnosis images and improving the efficiency of image processing.

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“…Therefore, the thresholds of the three algorithms in this paper are set to β1=0.46, β2=0.64, and β3=0.50. The experiment in this section uses the control variable method to adjust the weights of the weighted fusion model, selects the recall rates when the semantic similarity threshold is different values as the evaluation indicators and changes the value combination of a, b, and c in formula (17). We determine the optimal combination of fusion weights by observing the accuracy of the model.…”

Section: ) Similarity Threshold Selectionmentioning

confidence: 99%

“…Xue [13] compared the performance of LBM [14], FCN-8 [15], and Deeplabv3 [16] in the tongue segmentation task. Li proposed a multineural network hybrid learning method to achieve tongue recognition [17]. However, tongue images are not easy to collect, and the amount of data is usually very small.…”

Section: Introductionmentioning

confidence: 99%

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

Yuan

¹

,

Liao

²

2021

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“…Machine learning has been widely used in the field of tongue diagnosis, but the current research focuses on the field of supervised learning, which requires manual calibration of the tongue image [6][7][8]. It is not difficult to label data with clear diagnostic criteria.…”

Section: Introductionmentioning

confidence: 99%

Research of the Distribution of Tongue Features of Diabetic Population Based on Unsupervised Learning Technology

Li

¹

,

Cui

²

,

Tu

³

et al. 2022

Evidence-Based Complementary and Alternative Medicine

3

0

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Background. The prevalence of diabetes increases year by year, posing a severe threat to human health. Current treatments are difficult to prevent the progression of diabetes and its complications. It is imperative to carry out individualized treatment of diabetes, but current diagnostic methods are difficult to specify an individualized treatment plan. Objective. Clarify the distribution law of tongue features of the diabetic population, and provide the diagnostic basis for individualized treatment of traditional Chinese medicine (TCM) in the treatment of diabetes. Methods. We use the TFDA-1 tongue diagnosis instrument to collect tongue images of people with diabetes and accurately calculate the color features, texture features, and tongue coating ratio features through the Tongue Diagnosis Analysis System (TDAS). Then, we used K-means and Self-organizing Maps (SOM) networks to analyze the distribution of tongue features in diabetic people. Statistical analysis of TDAS features was used to identify differences between clusters. Results. The silhouette coefficient of the K-means clustering result is 0.194, and the silhouette coefficient of the SOM clustering result is 0.127. SOM Cluster 3 and Cluster 4 are derived from K-means Cluster 1, and the intersections account for (76.7% 97.5%) and (22.3% and 70.4%), respectively. K-means Cluster 2 and SOM Cluster 1 are highly overlapping, and the intersection accounts for the ratios of 66.9% and 95.0%. K-means Cluster 3 and SOM Cluster 2 are highly overlaid, and the intersection ratio is 94.1% and 82.1%. For the clustering results of K-means, TB-a and TC-a of Cluster 3 are the highest ( P < 0.001 ), TB-a of Cluster 2 is the lowest ( P < 0.001 ), and TB-a of Cluster 1 is between Cluster 2 and Cluster 3 ( P < 0.001 ). Cluster 1 has the highest TB-b and TC-b ( P < 0.001 ), Cluster 2 has the lowest TB-b and TC-b ( P < 0.001 ), and TB-b and TC-b of Cluster 3 are between Cluster 1 and Cluster 2 ( P < 0.001 ). Cluster 1 has the highest TB-ASM and TC-ASM ( P < 0.001 ), Cluster 3 has the lowest TB-ASM and TC-ASM ( P < 0.001 ), and TB-ASM and TC-ASM of Cluster 2 are between the Cluster 1 and Cluster 3 ( P < 0.001 ). CON, ENT, and MEAN show the opposite trend. Cluster 2 had the highest Per-all ( P < 0.001 ). SOM divides K-means Cluster 1 into two categories. There is almost no difference in texture features between Cluster 3 and Cluster 4 in the SOM clustering results. Cluster 3’s TB-L, TC-L, and Per-all are lower than Cluster 4 ( P < 0.001 ), Cluster 3’s TB-a, TC-a, TB-b, TC-b, and Per-part are higher than Cluster 4 ( P < 0.001 ). Conclusions. The precise tongue image features calculated by TDAS are the basis for characterizing the disease state of diabetic people. Unsupervised learning technology combined with statistical analysis is an important means to discover subtle changes in the tongue features of diabetic people. The machine vision analysis method based on unsupervised machine learning technology realizes the classification of the diabetic population based on fine tongue features. It provides a diagnostic basis for the designated diabetes TCM treatment plan.

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Natural tongue physique identification using hybrid deep learning methods

Cited by 24 publications

References 33 publications

Research and application of tongue and face diagnosis based on deep learning

Research and application of tongue and face diagnosis based on deep learning

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

Research of the Distribution of Tongue Features of Diabetic Population Based on Unsupervised Learning Technology

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