Deep sparse transfer learning for remote smart tongue diagnosis

Zhang, Xu; Huang, Wei; Gao, Jing; Wang, Dapeng; Bai, Chen; Chen, Zhikui

doi:10.3934/mbe.2021063

Cited by 3 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the symptoms on the tongue are very difficult to automatically identify or quantify, which has become a core challenge in SD. In recent years, many studies in this area have paid much attention to using deep learning methods to automatically classify or identify tongue symptoms, like tooth-marked tongue, 11 , 15 , 103 – 105 , 110 , 111 tongue coating, 10 , 16 , 31 , 42 , 112 , 113 coating color, 66 , 113 , 114 tongue color, 22 , 113 , 115 cracked tongue, 9 , 110 , 111 , 116 , 117 sublingual vein, 10 and fungiform papillae on tongue. 118 …”

Section: Tongue Image For the Symptom (Sign) Differentiationmentioning

confidence: 99%

“…Song et al 111 utilized a pretrained model training from ImageNet to classify tooth-marked, cracked, and thick coating, which obtained a compatible result when compared with ResNet50 and Inception_v3. Zhang et al 113 reported that the transfer learning method was used to differentiate tongue body color, coating color, and coating thickness for remote tongue diagnosis. Multitask learning could solve the classification and location tasks together; Weng et al 110 proposed a weakly supervised method to perform a coarse classification of tooth-marked and cracked tongues first and then a detection branch to locate the position of the features.…”

Section: Tongue Image For the Symptom (Sign) Differentiationmentioning

confidence: 99%

See 1 more Smart Citation

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

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.

show abstract

Section: Tongue Image For the Symptom (Sign) Differentiationmentioning

confidence: 99%

Section: Tongue Image For the Symptom (Sign) 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

Development and Application of Traditional Chinese Medicine Using AI Machine Learning and Deep Learning Strategies

Pan,

Guo,

Fan

et al. 2024

Am. J. Chin. Med.

View full text Add to dashboard Cite

Traditional Chinese medicine (TCM) has been used for thousands of years and has been proven to be effective at treating many complicated illnesses with minimal side effects. The application and advancement of TCM are, however, constrained by the absence of objective measuring standards due to its relatively abstract diagnostic methods and syndrome differentiation theories. Ongoing developments in machine learning (ML) and deep learning (DL), specifically in computer vision (CV) and natural language processing (NLP), offer novel opportunities to modernize TCM by exploring the profound connotations of its theory. This review begins with an overview of the ML and DL methods employed in TCM; this is followed by practical instances of these applications. Furthermore, extensive discussions emphasize the mature integration of ML and DL in TCM, such as tongue diagnosis, pulse diagnosis, and syndrome differentiation treatment, highlighting their early successful application in the TCM field. Finally, this study validates the accomplishments and addresses the problems and challenges posed by the application and development of TCM powered by ML and DL. As ML and DL techniques continue to evolve, modern technology will spark new advances in TCM.

show abstract

Artificial Intelligence in Tongue Image Recognition

Chu,

Ji,

Zhu

et al. 2023

International Journal of Software Science and Computational Intelligence

View full text Add to dashboard Cite

Tongue image recognition is a traditional Chinese medicine diagnosis method, which uses the shape, color, and texture of the tongue to judge the health of the human body. With the rapid development of artificial intelligence technology, the application of artificial intelligence in the field of tongue recognition has been widely considered. Based on the intelligent analysis of tongue diagnosis in traditional Chinese medicine, this paper reviews the application progress of artificial intelligence in tongue image recognition in recent years and analyzes its potential and challenges in this field. Firstly, this paper introduces three steps of tongue image recognition, including tongue image acquisition, tongue image preprocessing, and tongue image feature analysis. The application of traditional methods and artificial intelligence methods in the whole process of tongue image recognition is reviewed, especially the tongue body segmentation, and the advantages and disadvantages of convolutional neural networks are analyzed and compared. Artificial intelligence can use technologies such as deep learning and computer vision to automatically analyze and extract features from tongue images. By constructing a tongue image recognition model, tongue shape, color, texture, and other features can be accurately recognized and quantitatively analyzed. Finally, this paper summarizes the problems existing in artificial intelligence in tongue image recognition and looks forward to the future developmental direction of this field. It can promote the modernization of TCM diagnostic methods, achieve early disease screening and prevention, personalized medicine and treatment optimization, and support medical research and knowledge accumulation. However, there is still a need for further validation and practice, with a focus on patient privacy and data security.

show abstract

Deep sparse transfer learning for remote smart tongue diagnosis

Cited by 3 publications

References 30 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

Development and Application of Traditional Chinese Medicine Using AI Machine Learning and Deep Learning Strategies

Artificial Intelligence in Tongue Image Recognition

Contact Info

Product

Resources

About