Machine learning algorithms in classifying TCM tongue features in diabetes mellitus and symptoms of gastric disease

Fan, Shangyong; Chen, Bin; Zhang, Xirui; Hu, Xiaojuan; Bao, Lingshan; Yang, Xiaodong; Liu, Zhaobang; Yu, Yong

doi:10.1016/j.eujim.2021.101288

Cited by 14 publications

(9 citation statements)

References 11 publications

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“…But this method is unable to classify the multiple diseases from tongue images. In [20] authors used the machine learning models for classify the diabetes mellitus and symptoms of gastric disease from tongue images. Here, SVM recursive feature elimination (SVM-RFE) is used to extract the optimal color and texture features.…”

Section: Related Workmentioning

confidence: 99%

CATDSNet: Computer Aided Tongue Diagnosis System for Disease Prediction Using Hybrid Extreme Learning Machine

2023

IJIES

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Tongue diagnosis is an important way of diagnosing the human health in Indian ayurvedic medicine (IAM), which helps to identify different diseases through tongue image analysis. Traditionally, this diagnosis system has been done by ayurvedic experts to recognize the type of health issue by observing the human tongue. But the recent advancements in machine learning algorithms in medical field enabled the researchers to implement a computer aided diagnosis system for ayurvedic medical treatment. However, the existing tongue diagnosis systems are suffering with poor classification performance due to the variations in tongue appearance such as color, shape, coating, and texture properties. Therefore, this article focuses on computer aided tongue diagnosis system (CATDS) for disease predication through tongue image analysis, here after denoted as CATDSNet. Initially, fast nonlocal mean (FNLM) filtering is applied on test image to perform the preprocessing of given tongue dataset. Next, color features are extracted from the pre-processed tongue images using colour moments. In addition, grey level cooccurrence matrix (GLCM) is used to extract the texture features information. Then, this extracted color, and texture features are used to train the proposed CATDSNet using hybrid extreme learning machine (HELM) classifier for classifying various diseases such as healthy, appendicitis, bronchitis, gastritis, heart disease, and pancreatitis. The obtained simulation results on tongue image dataset disclose the superiority of proposed CATDSNet model as compared to state-of-the art approaches such as random forest, support vector machine (SVM), and SVM-based recursive feature elimination (SVM-RFE) with a classification accuracy of 92.3%, precision of 92.39%, recall of 92.06% and F1-score of 92.02%.

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Section: Related Workmentioning

confidence: 99%

CATDSNet: Computer Aided Tongue Diagnosis System for Disease Prediction Using Hybrid Extreme Learning Machine

2023

IJIES

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“…Fan et al [25] examined various tongue features in person with diabetic, gastric symptom utilize gathered images by digital tongue imaging. In feature extraction phase, texture and 4 TCM tongue features have been detected: coating colour, slenderness, cracks, constitution colour, and plumpness.…”

Section: Literature Surveymentioning

confidence: 99%

Internet of Things and Synergic Deep Learning Based Biomedical Tongue Color Image Analysis for Disease Diagnosis and Classification

2021

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In recent times, internet of things (IoT) and wireless communication techniques become widely used in healthcare sector. Biomedical image processing is commonly employed to detect the existence of diseases using biomedical images. Tongue diagnosis is an efficient, non-invasive model to perform auxiliary diagnosis any time anywhere that is support the global necessity in the primary healthcare system. Conventionally, medical practitioners investigate the tongue features based on their expert's knowledge comes from experience. In order to eradicate the qualitative aspects, tongue images can be quantitatively examined, offering an effective disease diagnostic process in such a way that the physical harm of the patients can be minimized. Numerous tongue image analysis approaches exist in the literature, it is required to develop automated deep learning (DL) models to diagnose the diseases using tongue image analysis. In this view, this paper designs an automated IoT and synergic deep learning based tongue color image (ASDL-TCI) analysis model for disease diagnosis and classification. The proposed ASDL-TCI model operates on major stages namely data acquisition, pre-processing, feature extraction, classification, and parameter optimization. Primarily, the IoT devices are used to capture the human tongue images and transmitted to the cloud for further analysis. In addition, median filtering based image pre-processing and SDL based feature extraction techniques are employed. Moreover, deep neural network (DNN) based classifier is applied to determine the existence of the diseases. Lastly, enhanced black widow optimization (EBWO) based parameter tuning process takes place to enhance the diagnostic performance. For assessing the effectual performance of the ASDL-TCI model, a set of simulations take place on benchmark tongue images and examined the results under distinct dimensions. The simulation outcome verified the enhanced diagnostic performance of the ASDL-TCI model over the compared methods with the maximum precision, recall, and accuracy of 0.984, 0.973, and 0.983. INDEX TERMSBiomedical image processing, Tongue color image analysis, Deep learning, Deep neural network, Parameter tuning, Synergic Deep Learning, Internet of Things.

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“…Fan et al [14] studied a number of tongue features in patients with gastric and diabetic symptoms. The researchers collected the images with the help of digital tongue imaging technique.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

Thanikachalam¹,

Shanthi²,

Kalirajan³

et al. 2022

Computers, Materials &Amp; Continua

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Machine learning algorithms in classifying TCM tongue features in diabetes mellitus and symptoms of gastric disease

Cited by 14 publications

References 11 publications

CATDSNet: Computer Aided Tongue Diagnosis System for Disease Prediction Using Hybrid Extreme Learning Machine

CATDSNet: Computer Aided Tongue Diagnosis System for Disease Prediction Using Hybrid Extreme Learning Machine

Internet of Things and Synergic Deep Learning Based Biomedical Tongue Color Image Analysis for Disease Diagnosis and Classification

Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

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