Log in

Relevant bibliographies by topics / Tongue Image Classification / Journal articles

To see the other types of publications on this topic, follow the link: Tongue Image Classification.

Journal articles on the topic 'Tongue Image Classification'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Tongue Image Classification.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Yan, Jianjun, Bochang Chen, Rui Guo, et al. "Tongue Image Texture Classification Based on Image Inpainting and Convolutional Neural Network." Computational and Mathematical Methods in Medicine 2022 (December 15, 2022): 1–11. http://dx.doi.org/10.1155/2022/6066640.

Full text

Abstract:

Tongue texture analysis is of importance to inspection diagnosis in traditional Chinese medicine (TCM), which has great application and irreplaceable value. The tough and tender classification for tongue image relies mainly on image texture of tongue body. However, texture discontinuity adversely affects the classification of the tough and tender tongue classification. In order to promote the accuracy and robustness of tongue texture analysis, a novel tongue image texture classification method based on image inpainting and convolutional neural network is proposed. Firstly, Gaussian mixture mod

APA, Harvard, Vancouver, ISO, and other styles

2

Zhang, David, Bo Pang, Naimin Li, Kuanquan Wang, and Hongzhi Zhang. "Computerized Diagnosis from Tongue Appearance Using Quantitative Feature Classification." American Journal of Chinese Medicine 33, no. 06 (2005): 859–66. http://dx.doi.org/10.1142/s0192415x05003466.

Full text

Abstract:

This study investigates relationships between diseases and the appearance of the human tongue in terms of quantitative features. The experimental samples are digital tongue images captured from three groups of candidates: one group in normal health, one suffering with appendicitis, and a third suffering with pancreatitis. For the purposes of diagnostic classification, we first extract chromatic and textural measurements from original tongue images. A feature selection procedure then identifies the measures most relevant to the classifications, based on which of the three tongue image categorie

APA, Harvard, Vancouver, ISO, and other styles

3

Liu, Bin, Zeya Wang, Kang Yu, et al. "Research on Tongue Image Segmentation and Classification Methods Based on Deep Learning and Machine Learning." Information 16, no. 5 (2025): 357. https://doi.org/10.3390/info16050357.

Full text

Abstract:

Tongue diagnosis is a crucial method in traditional Chinese medicine (TCM) for obtaining information about a patient’s health condition. In this study, we propose a tongue image segmentation method based on deep learning and a pixel-level tongue color classification method utilizing machine learning techniques such as support vector machine (SVM) and ridge regression. These two approaches together form a comprehensive framework that spans from tongue image acquisition to segmentation and analysis. This framework provides an objective and visualized representation of pixel-wise classification a

APA, Harvard, Vancouver, ISO, and other styles

4

Wang, Zhi Chun, Shi Ping Zhang, Pong Chi Yuen, et al. "Intra-Rater and Inter-Rater Reliability of Tongue Coating Diagnosis in Traditional Chinese Medicine Using Smartphones: Quasi-Delphi Study." JMIR mHealth and uHealth 8, no. 7 (2020): e16018. http://dx.doi.org/10.2196/16018.

Full text

Abstract:

Background There is a growing trend in the use of mobile health (mHealth) technologies in traditional Chinese medicine (TCM) and telemedicine, especially during the coronavirus disease (COVID-19) outbreak. Tongue diagnosis is an important component of TCM, but also plays a role in Western medicine, for example in dermatology. However, the procedure of obtaining tongue images has not been standardized and the reliability of tongue diagnosis by smartphone tongue images has yet to be evaluated. Objective The first objective of this study was to develop an operating classification scheme for tongu

APA, Harvard, Vancouver, ISO, and other styles

5

Xie, Jiacheng, Congcong Jing, Ziyang Zhang, Jiatuo Xu, Ye Duan, and Dong Xu. "Digital tongue image analyses for health assessment." Medical Review 1, no. 2 (2021): 172–98. http://dx.doi.org/10.1515/mr-2021-0018.

Full text

Abstract:

Abstract Traditional Chinese Medicine (TCM), as an effective alternative medicine, utilizes tongue diagnosis as a major method to assess the patient’s health status by examining the tongue’s color, shape, and texture. Tongue images can also give the pre-disease indications without any significant disease symptoms, which provides a basis for preventive medicine and lifestyle adjustment. However, traditional tongue diagnosis has limitations, as the process may be subjective and inconsistent. Hence, computer-aided tongue diagnoses have a great potential to provide more consistent and objective he

APA, Harvard, Vancouver, ISO, and other styles

6

Feng, Li, Zong Hai Huang, Yan Mei Zhong, et al. "Research and application of tongue and face diagnosis based on deep learning." DIGITAL HEALTH 8 (January 2022): 205520762211244. http://dx.doi.org/10.1177/20552076221124436.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

7

Kamarudin, Nur Diyana, Chia Yee Ooi, Tadaaki Kawanabe, Hiroshi Odaguchi, and Fuminori Kobayashi. "A Fast SVM-Based Tongue’s Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis." Journal of Healthcare Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/7460168.

Full text

Abstract:

In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye’s ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue’s multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k-means clusteri

APA, Harvard, Vancouver, ISO, and other styles

8

Hu, Junwei, Zhuangzhi Yan, and Jiehui Jiang. "Classification of fissured tongue images using deep neural networks." Technology and Health Care 30 (February 25, 2022): 271–83. http://dx.doi.org/10.3233/thc-228026.

Full text

Abstract:

BACKGROUND: Tongue inspection is vital in traditional Chinese medicine. Fissured tongue is an important feature in tongue diagnosis, and primarily corresponds to three Chinese medicine syndromes: syndrome-related hotness, blood deficiency, and insufficiency of the spleen. Diagnosis of the syndrome is significantly affected by the experience of clinicians, and it is difficult for young doctors to perform accurate diagnoses. OBJECTIVE: The syndrome not only depends on the local features based on fissured regions but also on the global features of the whole tongue; therefore, a syndrome diagnosis

APA, Harvard, Vancouver, ISO, and other styles

9

Qi, Zhen, Li-ping Tu, Jing-bo Chen, Xiao-juan Hu, Jia-tuo Xu, and Zhi-feng Zhang. "The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine." BioMed Research International 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/3510807.

Full text

Abstract:

Background and Goal. The application of digital image processing techniques and machine learning methods in tongue image classification in Traditional Chinese Medicine (TCM) has been widely studied nowadays. However, it is difficult for the outcomes to generalize because of lack of color reproducibility and image standardization. Our study aims at the exploration of tongue colors classification with a standardized tongue image acquisition process and color correction. Methods. Three traditional Chinese medical experts are chosen to identify the selected tongue pictures taken by the TDA-1 tongu

APA, Harvard, Vancouver, ISO, and other styles

10

Dushyant, Mankar. "Tongue Image Diagnosis System using Machine Learning with Hand-Crafted Features." International Journal of Preventive Medicine and Health (IJPMH) 4, no. 6 (2024): 1–6. https://doi.org/10.54105/ijpmh.L1097.04060924.

Full text

Abstract:

<strong>Abstract: </strong>Traditional Chinese Medicine theorizes a clear relationship between the visual characteristics of the tongue and the operational condition of the body's organs. The visual characteristics of the tongue can offer important indications for diagnosing diseases. Investigating tongue image processing methods for automated disease identification is a flourishing field of study in the modernization of Traditional Chinese Medicine. Although autonomous extraction of high-dimensional features is inherently more beneficial in deep learning than in conventional methods, its usef

APA, Harvard, Vancouver, ISO, and other styles

11

Lin, Jingjing, and Ruijuan Zheng. "A Review on the Classification of Traditional Chinese Medicine Tongue Images Based on Computer Technology." Journal of Computing and Electronic Information Management 10, no. 3 (2023): 62–64. http://dx.doi.org/10.54097/jceim.v10i3.8684.

Full text

Abstract:

Tongue imaging in traditional Chinese medicine is one of the important indicators for diagnosis, and its research has become a hot topic in the field of traditional Chinese medicine. In recent years, with the continuous development of computer technology, more and more research has applied computer technology to the analysis and diagnosis of tongue image classification in traditional Chinese medicine, which is of great significance for the diagnosis and treatment of traditional Chinese medicine. The research on the classification of tongue images is also of great importance. This article revie

APA, Harvard, Vancouver, ISO, and other styles

12

Zhu, Ming Feng, and Jian Qiang Du. "A Novel Approach for Color Tongue Image Extraction Based on Random Walk Algorithm." Applied Mechanics and Materials 462-463 (November 2013): 338–42. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.338.

Full text

Abstract:

Tongue image extraction is a fundamental step in objective diagnoses and quantitive checking of tongues. The accuracy of tongue image extraction can directly influence the results of the succedent checking in objective diagnoses of tongues. In this paper, we improved random walk image segmentation algorithm and applied it to tongue image extraction. Firstly, we utilized toboggan algorithm which adopted new classification rules to segment initial regions. Secondly, a weighted-graph was built according to initial regions in which only those adjacent regions were connected. Thirdly, random walk a

APA, Harvard, Vancouver, ISO, and other styles

13

Zhang, Guang, Xueying He, Delin Li, Cuihuan Tian, and Benzheng Wei. "Automated Screening of COVID-19-Based Tongue Image on Chinese Medicine." BioMed Research International 2022 (June 23, 2022): 1–12. http://dx.doi.org/10.1155/2022/6825576.

Full text

Abstract:

Objective. Artificial intelligence-powered screening systems of coronavirus disease 2019 (COVID-19) are urgently demanding since the ongoing outbreak of SARS-CoV-2 worldwide. Chest CT or X-ray is not sufficient to support the large-scale screening of COVID-19 because mildly-infected patients do not have imaging features on these images. Therefore, it is imperative to exploit supplementary medical imaging strategies. Traditional Chinese medicine has played an essential role in the fight against COVID-19. Methods. In this paper, we conduct two kinds of verification experiments based on a newly-c

APA, Harvard, Vancouver, ISO, and other styles

14

Yang, Yan, Yunxia Yin, and Zhi Li. "Research on the model of automatic recognition and natural language question-answer system for traditional Chinese medicine tongue images based on LLMs." Applied and Computational Engineering 36, no. 1 (2024): 271–77. http://dx.doi.org/10.54254/2755-2721/36/20230461.

Full text

Abstract:

Large Language Models (LLMs) have recently demonstrated their potential in clinical applications by providing valuable medical knowledge and recommendations. Traditional Chinese tongue diagnosis, one of the "Four Diagnoses," is an essential method for traditional Chinese medicine diagnosis. This paper builds upon tongue image classification technology and utilizes natural language processing and image recognition techniques to enhance the discrimination and analysis of traditional Chinese tongue images through learning and inference. We propose a method to integrate LLMs into the tongue image

APA, Harvard, Vancouver, ISO, and other styles

15

Xiang, Zhengke, Min Tang, Huan Yang, and Congrong Tan. "Application of Convolutional Neural Network Algorithm in Diagnosis of Chronic Cough and Tongue in Children with Traditional Chinese Medicine." Journal of Medical Imaging and Health Informatics 10, no. 2 (2020): 401–9. http://dx.doi.org/10.1166/jmihi.2020.2881.

Full text

Abstract:

Objective: This paper learns and studies the structure of convolutional neural network in deep learning, automatically extracts feature information, and explores the feasibility of this method in the classification model of chronic cough and tongue in children of traditional Chinese medicine, and assists in further objective analysis of tongue diagnosis of traditional Chinese medicine. Chemical. Through the research on the relationship between children's cough tongue and TCM syndrome type, severity of illness, disease course and laboratory examination, it provides objective basis for clinical

APA, Harvard, Vancouver, ISO, and other styles

16

Yan, Bo, Sheng Zhang, Zijiang Yang, Hongyi Su, and Hong Zheng. "Tongue Segmentation and Color Classification Using Deep Convolutional Neural Networks." Mathematics 10, no. 22 (2022): 4286. http://dx.doi.org/10.3390/math10224286.

Full text

Abstract:

Tongue color classification serves as important assistance for traditional Chinese medicine (TCM) doctors to make a precise diagnosis. This paper proposes a novel two-step framework based on deep learning to improve the performance of tongue color classification. First, a semantic-based CNN called SegTongue is applied to segment the tongues from the background. Based on DeepLabv3+, multiple atrous spatial pyramid pooling (ASPP) modules are added, and the number of iterations of fusions of low-level and high-level information is increased. After segmentation, various classical feature extractio

APA, Harvard, Vancouver, ISO, and other styles

17

Bhatnagar, Vibha, and Prashant P. Bansod. "Convolution Neural Network Based Multi-Label Disease Detection Using Smartphone Captured Tongue Images." Applied Sciences 14, no. 10 (2024): 4208. http://dx.doi.org/10.3390/app14104208.

Full text

Abstract:

Purpose: Tongue image analysis for disease diagnosis is an ancient, traditional, non-invasive diagnostic technique widely used by traditional medicine practitioners. Deep learning-based multi-label disease detection models have tremendous potential for clinical decision support systems because they facilitate preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline where observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. Subjects, who consult collaborating physicians,

APA, Harvard, Vancouver, ISO, and other styles

18

Wei, L. I. U., C. H. E. N. Jinming, L. I. U. Bo, H. U. Wei, W. U. Xingjin, and Z. H. O. U. Hui. "Tongue image segmentation and tongue color classification based on deep learning." Digital Chinese Medicine 5, no. 3 (2022): 253–63. http://dx.doi.org/10.1016/j.dcmed.2022.10.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Yang, Zibin, Yuping Zhao, Jiarui Yu, Xiaobo Mao, Huaxing Xu, and Luqi Huang. "An Intelligent Tongue Diagnosis System via Deep Learning on the Android Platform." Diagnostics 12, no. 10 (2022): 2451. http://dx.doi.org/10.3390/diagnostics12102451.

Full text

Abstract:

To quickly and accurately identify the pathological features of the tongue, we developed an intelligent tongue diagnosis system that uses deep learning on a mobile terminal. We also propose an efficient and accurate tongue image processing algorithm framework to infer the category of the tongue. First, a software system integrating registration, login, account management, tongue image recognition, and doctor–patient dialogue was developed based on the Android platform. Then, the deep learning models, based on the official benchmark models, were trained by using the tongue image datasets. The t

APA, Harvard, Vancouver, ISO, and other styles

20

Jiang, Tao, Zhou Lu, Xiaojuan Hu, et al. "Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup." Evidence-Based Complementary and Alternative Medicine 2022 (September 29, 2022): 1–12. http://dx.doi.org/10.1155/2022/3384209.

Full text

Abstract:

Background. Research on intelligent tongue diagnosis is a main direction in the modernization of tongue diagnosis technology. Identification of tongue shape and texture features is a difficult task for tongue diagnosis in traditional Chinese medicine (TCM). This study aimed to explore the application of deep learning techniques in tongue image analyses. Methods. A total of 8676 tongue images were annotated by clinical experts, into seven categories, including the fissured tongue, tooth-marked tongue, stasis tongue, spotted tongue, greasy coating, peeled coating, and rotten coating. Based on th

APA, Harvard, Vancouver, ISO, and other styles

21

Zhang, Bob, and Han Zhang. "Significant Geometry Features in Tongue Image Analysis." Evidence-Based Complementary and Alternative Medicine 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/897580.

Full text

Abstract:

The shape of a human tongue and its relation to a patients’ state, either healthy or diseased (and if diseased which disease), is quantitatively analyzed using geometry features by means of computerized methods in this paper. Thirteen geometry features based on measurements, distances, areas, and their ratios are extracted from tongue images captured by a specially designed device with color correction. Using the features, 5 tongue shapes (rectangle, acute and obtuse triangles, square, and circle) are defined based on traditional Chinese medicine (TCM). Classification of the shapes is subseque

APA, Harvard, Vancouver, ISO, and other styles

22

Wang, Shasha, and Xiaoyi Zhao. "Deep Learning in Tongue Diagnosis for Traditional Chinese Medicine: A Review." Frontiers in Computing and Intelligent Systems 12, no. 1 (2025): 139–43. https://doi.org/10.54097/mq406m63.

Full text

Abstract:

With the rapid development of artificial intelligence, deep learning has become an important tool in medical image analysis. Tongue image analysis is a crucial component of the objectification of tongue diagnosis in Traditional Chinese Medicine (TCM). However, traditional tongue diagnosis methods primarily rely on the experience and judgment of practitioners and can be easily influenced by external environmental factors. Therefore, the objectification and standardization of TCM tongue diagnosis has become an inevitable trend in its development. This paper systematically reviews recent research

APA, Harvard, Vancouver, ISO, and other styles

23

许, 志磊. "Tongue Image Classification Based on Gray-scale Difference." Computer Science and Application 10, no. 02 (2020): 190–99. http://dx.doi.org/10.12677/csa.2020.102020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Damkliang, Kasikrit, Jularat Chumnaul, Teerawat Sudkhaw, Thitinan Yingtawee, and Nasma Saearm. "Multi-Model Approach for Tongue Image Classification in Traditional Thai Medicine." International Journal of Online and Biomedical Engineering (iJOE) 21, no. 05 (2025): 47–62. https://doi.org/10.3991/ijoe.v21i05.53671.

Full text

Abstract:

Nowadays, complementary medicine is gaining widespread acceptance and is widely accepted, particularly within traditional Thai medicine (TTM). Tongue inspection is a primary method for diagnosing health conditions, as it reflects organ functionality. However, diagnostic results can vary depending on the expertise of TTM practitioners. In this work, we propose methods that incorporate transfer learning (TL) from deep learning (DL), machine learning (ML), and statistical models, using various tongue features. We introduced a collected dataset for evaluation. Experimental results demonstrated tha

APA, Harvard, Vancouver, ISO, and other styles

25

Kang, Chung Hwan, and Tae Soo Lee. "Grading Classification of Tongue Cancer in Oral Images Using CNN Transfer Learning." Journal of Medical Imaging 5, no. 1 (2022): 21–41. http://dx.doi.org/10.31916/sjmi2022-01-02.

Full text

Abstract:

- Oral cancer is one of the top 10 diseases in cancer incidence worldwide and its prognosis is not good as early diagnosis is not made and so the survival rate from it has not been improved greatly. Oral cancer occurs most commonly in tongue, floor of mouth, and lower lip and 5-year survival rate is very low at 50% and if diagnosed early, the average time of survival becomes longer. Therefore, to improve the survival rate of oral cancer patients, early diagnosis and discovery and patient habit improvement including smoking are very important. Oral cancer can be diagnosed with imaging equipment

APA, Harvard, Vancouver, ISO, and other styles

26

Meng, Dan, Guitao Cao, Ye Duan, et al. "Tongue Images Classification Based on Constrained High Dispersal Network." Evidence-Based Complementary and Alternative Medicine 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7452427.

Full text

Abstract:

Computer aided tongue diagnosis has a great potential to play important roles in traditional Chinese medicine (TCM). However, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by deep convolutional neural network (CNN), we propose a novel feature extraction framework called constrained high dispersal neural networks (CHDNet) to extract unbiased features and reduce human labor for tongue diagnosis in TCM. Previous CNN models have mostly focused on learning convolutional

APA, Harvard, Vancouver, ISO, and other styles

27

Yi, Tian-Xing, Jian-Xin Chen, Xue-Song Wang, Meng-Jie Kou, Qing-Qiong Deng, and Xu Wang. "Constructing an Artificial Intelligent Deep Neural Network Battery for Tongue Region Segmentation and Tongue Characteristic Recognition." World Journal of Traditional Chinese Medicine 10, no. 4 (2024): 460–64. https://doi.org/10.4103/wjtcm.wjtcm_92_24.

Full text

Abstract:

Abstract Objective: This study aimed to construct a two-stage deep learning framework to segment and recognize tongue images and enhance the accuracy and efficiency of artificial intelligence (AI) tongue diagnosis in traditional Chinese medicine (TCM). Materials and Methods: Five hundred and ninety-four tongue images of adequate quality were used to construct AI models. First, a multi-attention UNet model was used for semantic segmentation to distinguish the tongue body from the background. In the second stage, a residual network was employed to classify seven important tongue characteristics.

APA, Harvard, Vancouver, ISO, and other styles

28

Kanawong, Ratchadaporn, Tayo Obafemi-Ajayi, Tao Ma, Dong Xu, Shao Li, and Ye Duan. "Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine." Evidence-Based Complementary and Alternative Medicine 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/912852.

Full text

Abstract:

ZHENG, Traditional Chinese Medicine syndrome, is an integral and essential part of Traditional Chinese Medicine theory. It defines the theoretical abstraction of the symptom profiles of individual patients and thus, used as a guideline in disease classification in Chinese medicine. For example, patients suffering from gastritis may be classified as Cold or Hot ZHENG, whereas patients with different diseases may be classified under the same ZHENG. Tongue appearance is a valuable diagnostic tool for determining ZHENG in patients. In this paper, we explore new modalities for the clinical characte

APA, Harvard, Vancouver, ISO, and other styles

29

Shi, Yu Lin, Tao Jiang, Xiao Juan Hu, et al. "A Study of Logistic Regression for Fatigue Classification Based on Data of Tongue and Pulse." Evidence-Based Complementary and Alternative Medicine 2022 (March 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/2454678.

Full text

Abstract:

Background and Objective. Fatigue is a subjective symptom which is hard to quantify, it is prevalent in the subhealth and disease population, and there is still no accurate and stable method to distinguish disease fatigue from subhealth fatigue. Tongue diagnosis and pulse diagnosis are the reflection of the overall state of the body, and the modern research of tongue diagnosis and pulse diagnosis has made great progress. This study aims to explore the distribution rules of tongue and pulse data in a disease fatigue and subhealth fatigue population and evaluate the contribution rate of tongue a

APA, Harvard, Vancouver, ISO, and other styles

30

Li, Jun, Jingbin Huang, Tao Jiang, et al. "A multi-step approach for tongue image classification in patients with diabetes." Computers in Biology and Medicine 149 (October 2022): 105935. http://dx.doi.org/10.1016/j.compbiomed.2022.105935.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Dhanaraj, N., R. Sam Wesley, S. Santhiya, K. Sathya, and G. Sivasathya. "Disease Diagnosis by Means of Segmented tongue Image Classification Exhausting Neural Networks." i-manager’s Journal on Pattern Recognition 1, no. 1 (2014): 23–29. http://dx.doi.org/10.26634/jpr.1.1.2821.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Kiran, Chavan, Kadam Gayatri, Kankaria Ruchika, Kate Raksha, and Ladekar Ashvini. "Object Detection for Image Captioning." Journal of Image Processing and Artificial Intelligence 5, no. 1 (2019): 17–24. https://doi.org/10.5281/zenodo.2551870.

Full text

Abstract:

Generation of description of pictures victimization tongue sentences is gaining a lot of quality of late. It's a difficult task, because it needs not solely understanding a picture, however to translate that visual data into sentence description. So as to caption a picture, we tend to 1st have to be compelled to discover the objects within the image. Object detection has become one amongst the international widespread analysis fields. 1st the paper introduced the distinction between deep learning and machine learning for object detection. Second the techniques for object detection are surv

APA, Harvard, Vancouver, ISO, and other styles

33

Zhang, Bob, Xingzheng Wang, Jane You, and David Zhang. "Tongue Color Analysis for Medical Application." Evidence-Based Complementary and Alternative Medicine 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/264742.

Full text

Abstract:

An in-depth systematic tongue color analysis system for medical applications is proposed. Using the tongue color gamut, tongue foreground pixels are first extracted and assigned to one of 12 colors representing this gamut. The ratio of each color for the entire image is calculated and forms a tongue color feature vector. Experimenting on a large dataset consisting of 143 Healthy and 902 Disease (13 groups of more than 10 samples and one miscellaneous group), a given tongue sample can be classified into one of these two classes with an average accuracy of 91.99%. Further testing showed that Dis

APA, Harvard, Vancouver, ISO, and other styles

34

Biehl, Sarah C., Sarah Dugan, Sarah R. Li, et al. "Optimization of classifying accurate and misarticulated speech sounds for use in a gamified real-time ultrasound biofeedback system." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A335. http://dx.doi.org/10.1121/10.0027722.

Full text

Abstract:

Integrating ultrasound biofeedback therapy (UBT) into a real-time, gamified interface to provide articulatory feedback for speech remediation promotes an external focus of attention, thereby reducing the complex cognitive demands required for standard UBT. Previous studies have shown that accuracy of American English rhotic /r/ can be predicted by a single parameter, the difference between tongue dorsum and blade displacements measured by ultrasound imaging during speech production. This parameter has classified speech productions of rhotic syllables as correct versus misarticulated with a cla

APA, Harvard, Vancouver, ISO, and other styles

35

Devi, G. Uma, and E. A. Mary Anita. "A novel semi supervised learning algorithm for thyroid and ulcer classification in tongue image." Cluster Computing 22, S5 (2018): 11537–49. http://dx.doi.org/10.1007/s10586-017-1417-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Mansour, Romany F., Maha M. Althobaiti, and Amal Adnan Ashour. "Internet of Things and Synergic Deep Learning Based Biomedical Tongue Color Image Analysis for Disease Diagnosis and Classification." IEEE Access 9 (2021): 94769–79. http://dx.doi.org/10.1109/access.2021.3094226.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Lin, Xing Min, Luting Xia, and Xiaoyun Ye. "Thermal radiation of tongue surface as a human computer interaction diagnostics technique based on image classification with software interface." Journal of Radiation Research and Applied Sciences 17, no. 2 (2024): 100892. http://dx.doi.org/10.1016/j.jrras.2024.100892.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Aach, T., H. Witte, and T. M. Lehmann. "Sensor, Signal and Image Informatics." Yearbook of Medical Informatics 15, no. 01 (2006): 57–67. http://dx.doi.org/10.1055/s-0038-1638479.

Full text

Abstract:

SummaryThe number of articles published annually in the fields of biomedical signal and image acquisition and processing is increasing. Based on selected examples, this survey aims at comprehensively demonstrating the recent trends and developments.Four articles are selected for biomedical data acquisition covering topics such as dose saving in CT, C-arm X-ray imaging systems for volume imaging, and the replacement of dose-intensive CTbased diagnostic with harmonic ultrasound imaging. Regarding biomedical signal analysis (BSA), the four selected articles discuss the equivalence of different ti

APA, Harvard, Vancouver, ISO, and other styles

39

Li, Jun, Longtao Cui, Liping Tu, et al. "Research of the Distribution of Tongue Features of Diabetic Population Based on Unsupervised Learning Technology." Evidence-Based Complementary and Alternative Medicine 2022 (July 5, 2022): 1–14. http://dx.doi.org/10.1155/2022/7684714.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

40

Walther, Michael, Ulrich Kamp, Nyam-Osor Nandintsetseg, Avirmed Dashtseren, and Khurelbaatar Temujin. "Glacial Lakes of Mongolia." Geographies 4, no. 1 (2024): 21–39. http://dx.doi.org/10.3390/geographies4010002.

Full text

Abstract:

The over 2200 lakes of Mongolia are generally poorly studied, particularly the glacial lakes. This overview study presents a classification of the glacial lakes based on tectonic-geological and geomorphological dynamics. Selected representative lakes are described using results from fieldwork and satellite image analysis, including bathymetry, paleoshorelines, and recent lake-level fluctuations between 1987 and 2020. Generally, lake levels dropped from the early Holocene until recently, with the onset of the climate change-driven glacier recession that has resulted in lake-level rises and area

APA, Harvard, Vancouver, ISO, and other styles

41

Barrera, Jose E., Andrew B. Holbrook, Juan Santos, and Gerald R. Popelka. "Pulse Arterial Tone and Airway Obstruction in Sleep Apnea." Otolaryngology–Head and Neck Surgery 139, no. 2_suppl (2008): P83. http://dx.doi.org/10.1016/j.otohns.2008.05.268.

Full text

Abstract:

Objective Determine if continuous pulse arterial tone (PAT) amplitude correlates with upper airway obstructions observed during simultaneous real-time magnetic resonance imaging (RT-MRI) in subjects with Obstructive Sleep Apnea (OSA). Methods A prospective series of 20 subjects diagnosed with mild to severe OSA by polysomnography, Fujita classification, Functional Outcomes of Sleep Questionnaire (FOSQ) and Epworth Sleepiness Score (ESS) underwent continuous RT-MRI during a 90-minute nap without sedation. The upper airway at the mid-saggittal plane was visualized in real time (33 fps) using a s

APA, Harvard, Vancouver, ISO, and other styles

42

Chantaramanee, Ariya, Kazuharu Nakagawa, Kanako Yoshimi, Ayako Nakane, Kohei Yamaguchi, and Haruka Tohara. "Comparison of Tongue Characteristics Classified According to Ultrasonographic Features Using a K-Means Clustering Algorithm." Diagnostics 12, no. 2 (2022): 264. http://dx.doi.org/10.3390/diagnostics12020264.

Full text

Abstract:

The precise correlations among tongue function and characteristics remain unknown, and no previous studies have attempted machine learning-based classification of tongue ultrasonography findings. This cross-sectional observational study aimed to investigate relationships among tongue characteristics and function by classifying ultrasound images of the tongue using a K-means clustering algorithm. During 2017–2018, 236 healthy older participants (mean age 70.8 ± 5.4 years) were enrolled. The optimal number of clusters determined by the elbow method was 3. After analysis of tongue thickness and e

APA, Harvard, Vancouver, ISO, and other styles

43

Zhi, Liu, David Zhang, Jing-qi Yan, Qing-Li Li, and Qun-lin Tang. "Classification of hyperspectral medical tongue images for tongue diagnosis." Computerized Medical Imaging and Graphics 31, no. 8 (2007): 672–78. http://dx.doi.org/10.1016/j.compmedimag.2007.07.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

MISS., MINAL A. LOHAR, and K. R. DESAI DR. "DETECTION OF DIABETES MELLITUS WITH THE TONGUE FEATURES." IJIERT - International Journal of Innovations in Engineering Research and Technology 4, no. 4 (2017): 22–29. https://doi.org/10.5281/zenodo.1461205.

Full text

Abstract:

<strong>A non - invasive approach is proposed which uses color,texture and geometry features for diagnosis to detect diabetic mellitus. Diabetic Mellitus (DM) & its complication tow ardsretinopathy is world�s major health problem. This paper suggest technique for classifying health & DM samples by obtaining tongue images using capture device. By color space conversion & region based segmentation,images are pre - processed . Then the ex traction of features i.e.,color,texture & geometry is done. The classification of healthy(normal) /DM(abnormal) tongues are obtained by combinat

APA, Harvard, Vancouver, ISO, and other styles

45

Kale, Himanshu. "Disease Prediction by Tongue Classification using CNN." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 177–81. http://dx.doi.org/10.22214/ijraset.2023.56467.

Full text

Abstract:

Abstract: The integration of traditional medicine and modern technology has opened new avenues for disease prediction and diagnosis. In this study, we explore the use of tongue classification as a non-invasive and cost-effective approach to predict and diagnose various diseases. Tongue images were collected from a diverse patient population and processed to extract relevant features. Machine learning algorithms were employed to classify these tongue images into disease categories, yielding promising results. The study's findings demonstrate the potential of tongue classification as an efficien

APA, Harvard, Vancouver, ISO, and other styles

46

Li, Sarah R., T. Douglas Mast, and Suzanne Boyce. "Quantifying tongue tip visibility in ultrasound images of /r/ tongue shapes using numerical ultrasound simulations." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A372. http://dx.doi.org/10.1121/10.0019217.

Full text

Abstract:

Midsagittal ultrasound images provide important articulatory information about tongue shape by showing much of the tongue surface from the root to the tip. However, portions of the tongue tip are often obscured due to shadowing from the mandible bone and the sublingual airspace. Information about tongue tip position can be important for understanding tongue movement patterns, e.g., classification of a retroflex or bunched shape for the American English rhotic /r/. Here, numerical simulations of acoustic wave propagation in tissue were performed using the open-source k-Wave toolbox to recreate

APA, Harvard, Vancouver, ISO, and other styles

47

Hu, Xiaojuan, Zhaobang Liu, Xiaodong Yang, et al. "An Unsupervised Tongue Segmentation Algorithm Based on Improved gPb-owt-ucm." Journal of Medical Imaging and Health Informatics 11, no. 3 (2021): 688–96. http://dx.doi.org/10.1166/jmihi.2021.3317.

Full text

Abstract:

Background and Objective: The modernization of tongue diagnosis is an important research in Traditional Chinese Medicine. Accurate and practical tongue segmentation method is a premise in subsequent analyses. In this paper, an unsupervised tongue segmentation method is proposed based on an improved gPb-owt-ucm algorithm. The gPb-owt-ucm is short for global pixel point, oriented watershed transform and ultrametric contour map. Methods: Improved gPb-owt-ucm algorithm is adopted in this paper because of its powerful contour detection capabilities. The boundary feasibility of each pixel is calcula

APA, Harvard, Vancouver, ISO, and other styles

48

Pradkhshana, Vijay, Sharma Supriya, Chandra Shaleen, Chandra Shaleen, Singh Priyanka, and Srivastava Yash. "A Study on Evaluation of Various Tongue Patterns in North Indian Population and a Working Classification System for These Tongue Print Patterns." International Healthcare Research Journal 3, no. 2 (2019): 76–79. https://doi.org/10.26440/IHRJ/0302.05.521081.

Full text

Abstract:

<strong>INTRODUCTION:</strong> Tongue is a vital internal organ enclosed within the oral cavity and is well protected from the external environment. The color, shape, and surface features are characteristic of every individual, and this serves as a tool for identification. The search for a new personal identification method secure has led to the use of the tongue print as a method of biometric verification. <strong>AIM AND OBJECTIVE:</strong> To analyze the shape, margins, texture of tongue prints and compare these between males and females. Also, formulate a working classification system for

APA, Harvard, Vancouver, ISO, and other styles

49

Azhar, Yufis, Fauzan Adrivano Setiono, and Didih Rizki Chandranegara. "Comparison of Transfer Learning Models in Classification Dental and Tongue Disease Images." Journal of Electronics, Electromedical Engineering, and Medical Informatics 7, no. 1 (2024): 117–29. https://doi.org/10.35882/jeeemi.v7i1.487.

Full text

Abstract:

According to the Global Burden of Disease Study, dental caries is the most prevalent oral health ailment, affecting around 3.5 billion individuals globally. According to the Ministry of Health of the Republic of Indonesia, 93% of children in the country suffer from oral health issues, making poor oral health a serious public health concern. The tongue and teeth in the mouth are particularly vulnerable to a wide range of illnesses, and the condition of the mouth is a key sign of the health of the body as a whole. The CNN algorithm has been utilized in numerous studies to classify disorders of t

APA, Harvard, Vancouver, ISO, and other styles

50

Ni, Jinghong, Zhuangzhi Yan, and Jiehui Jiang. "TongueCaps: An Improved Capsule Network Model for Multi-Classification of Tongue Color." Diagnostics 12, no. 3 (2022): 653. http://dx.doi.org/10.3390/diagnostics12030653.

Full text

Abstract:

Tongue color is an important part of tongue diagnosis. The change of tongue color is affected by pathological state of body, blood rheology, and other factors. Therefore, physicians can understand a patient’s condition by observing tongue color. 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. Recently, Capsule Networks (CapsNet) have been proposed to overcome these

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!