Tongue Image Texture Classification Based on Image Inpainting and Convolutional Neural Network

Yan, Jianjun; Chen, Bochang; Guo, Rui; Zeng, Menghao; Yan, Hong‐Bin; Xu, Zhaoxia; Wang, Yiqin

doi:10.1155/2022/6066640

Cited by 8 publications

(5 citation statements)

References 19 publications

(22 reference statements)

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“…Qi [ 67 ] used the open-source Weka software to classify the color of 728 tongue images, and obtained an RF prediction accuracy of 84.94%. Yan [ 69 ] used deep learning and RF to classify normal, mild, and severe teeth-marked tongues. Lu [ 70 ] utilized Ridge-CNN to classify sublingual varices of TCM with an accuracy rate of 87.5%.…”

Section: Applications Of Machine Learning In Tcm Researchmentioning

confidence: 99%

Machine learning in TCM with natural products and molecules: current status and future perspectives

Liu

et al. 2023

Chin Med

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Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory.

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Section: Applications Of Machine Learning In Tcm Researchmentioning

confidence: 99%

Machine learning in TCM with natural products and molecules: current status and future perspectives

Liu

et al. 2023

Chin Med

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“…How to digitize, quantify, extract, and analyze tongue features in traditional Chinese medicine theory is the key to achieving digitalization and objectification of tongue diagnosis. In recent years, many scholars have started to study traditional Chinese medicine tongue diagnosis and proposed research methods to achieve objectification and intelligence of traditional Chinese medicine tongue diagnosis, achieving some results mainly including research on tongue image segmentation, texture features [16], [17] , color features [18], [19], [20], [21], and shape features [22], [23].…”

Section: Related Workmentioning

confidence: 99%

Syndrome Types Classification Method of Skin Diseases Based on Tongue Hierarchical Feature Fusion

Wang,

Wang

et al. 2023

Preprint

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Tongue diagnosis is a non-invasive, painless diagnostic method by observing the tongue image of patients to diagnose and analyze their pathological conditions, which provides an opportunity for the future development of tongue diagnosis. However, the traditional tongue diagnosis method mainly relies on the experience and judgment of doctors, and is also easily affected by external factors. These factors hinder the development and application of tongue diagnosis. Currently, most studies use machine learning, which is time consuming and labor intensive. Other studies use deep learning based on convolutional neural network (CNN), but the affine transformation of CNN is less robust and easily loses the spatial relationship between features. In this work, we propose a traditional Chinese medicine (TCM) syndrome classification model of skin diseases based on tongue image hierarchical feature fusion. By adding a multi-scale residual module to the feature extraction part of the capsule network, we can extracted richer feature of tongue image. At the same time, the attention mechanism module is embedded in the multi-scale residual module, with the help of the attention mechanism module, the interference of tongue impurity information is suppressed, and accurate features are extracted for classification. Through experiments, it has been proven that our proposed method has achieved accuracy of 89.6\% in the classification of tongue for acne syndrome, and accuracy of 91.6\% in the dermatitis syndrome.

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“…Correlational research including Dapeng Zhang (Tingting et al, 2016) put forward based on Gabor wavelet transforming tongue picture of tongue coating texture analysis of digital tongue coating texture analysis is focused on the management of digital tongue image analysis and identification of thin/thick and greasy tongue coating characteristics (namely the tongue coating is thin or thick, whether greasy tongue coating) (Lee et al, 2016b). Yan et al (2022) proposed a tongue image texture classification method based on image interpolation and convolutional neural network, and completed the classification of tough tongue, soft tongue, and normal tongue texture types. As described in 2.3.7, we proposed an improved split-tongue image segmentation model based on the U-Net model and constructed a database of split-tongue images (Li et al, 2021).…”

Section: Tongue Crackmentioning

confidence: 99%

Artificial Intelligence in Tongue Image Recognition

Chu,

Ji,

Zhu

et al. 2023

International Journal of Software Science and Computational Intelligence

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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.

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Tongue Image Texture Classification Based on Image Inpainting and Convolutional Neural Network

Cited by 8 publications

References 19 publications

Machine learning in TCM with natural products and molecules: current status and future perspectives

Machine learning in TCM with natural products and molecules: current status and future perspectives

Syndrome Types Classification Method of Skin Diseases Based on Tongue Hierarchical Feature Fusion

Artificial Intelligence in Tongue Image Recognition

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