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(a) Deep red cluster identified by maximum pixels’ coverage area identifier and (b) and (c) red/light red clusters identified by maximum colour distance identifier.
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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 es...
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Citations
... 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]. ...
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.
... For image segmentation different researchers used different methods using supervised and unsupervised methods. K-mean clustering method is unsupervised method which is also being used [15][16][17] for segmentation. Its efficiency can be improved using algorithms like firefly algorithm, parallel processing or watershed algorithm [18][19][20]. ...
Due to the change in life style after covid-19 there is demand for non-invasive contact less healthcare monitoring systems. In India oral cancer rate are increasing and becoming the community health issue with high mortality rate. Mortality rate can be reduced by identification of disease at initial stages. The major hindrance in disease identification is availability of dedicated hardware at remote and identification at initial stage. Research is going on to make the device portable and less costly using digital images. Also, research is going on to have hybrid algorithm which can segment smaller area abnormal area and classify reliably. This paper focuses on tongue cancer which is one of the types of oral cancer and presents comparison of hybrid algorithm using firefly and watershed transformation to segment smaller area using digital images which reduces cost of dedicated hardware required. 150 digital images are used which are available on internet or provided by cancer hospital for analysis and classification using CNN along with augmentation. 90.48% accuracy is achieved and desirable results are obtained using hybrid algorithm being used.
... The tongue diagnosis relies on the personal experience of Chinese medicine practitioners and the surrounding environment. However, it lacks certain reproducibility and is difficult to be recognized by the majority of modern doctors (Kamarudin et al., 2017). It is needed to provide a corresponding biological basis for tongue diagnosis in patients. ...
Tongue diagnosis is a unique aspect of traditional Chinese medicine for diagnosing diseases before determining proper means of treatment, but it also has the disadvantage of relying on the subjective experience of medical practitioners and lack objective basis. The purpose of this article is to elucidate tongue-coating microbiota and metabolic differences in primary liver cancer (PLC) patients with thick or greasy tongue coatings. Tongue-coating samples were analyzed in 60 PLC patients (30 PLC with thick or greasy tongue-coating patients and 30 PLC with tongue-coating neither thick nor greasy) and 25 healthy controls (HC) using 16S rRNA gene sequencing technology. As compared to healthy individuals, tongue coatings of patients with PLC had elevated levels of Firmicutes and Actinobacteria. The abundance of Fusobacteria, SR1_Absconditabacteria_, and Spirochaete were higher in tongue coatings of healthy controls compared to samples in patients with PLC. In addition to site-specific differences, higher abundances of Fusobacteria and Actinobacteria were observed in thick or greasy tongue-coating patients as compared to non-thick and greasy tongue-coating patients. The inferred metagenomic pathways enriched in the PLC tongue-coating patients were mainly those involved in replication, recombination, and repair of protein. We also identify a tongue-coating microbiome signature to discriminate HC and PLC, including 15 variables on genus level. The prediction performance of the signature showed well in the training and validation cohorts. This research illustrates specific clinical features and bacterial structures in PLC patients with different tongue coatings, which facilitates understanding of the traditional tongue diagnosis.
... Because the features of different tongue color classifications are similar and tongue color diagnosis is more difficult than tongue coating and tongue shape, we will start with tongue color classification to study the feature extraction method of tongue color classification. Most tongue diagnosis works [14][15][16][17][18][19] adopt statistical methods and some traditional machine learning methods. However, the accuracy of these tongue color classification methods usually cannot meet the needs of actual diagnosis. ...
... Li et al. [15] presented an approach analyzing tongue color based on spectra with a spectral angle mapper. Kamarudin et al. [17] proposed a two-stage tongue classification method based on a support vector machine (SVM) whose support vectors are reduced by a k-means clustering identifier. Zhou et al. [19] adopted Content-Based Image Retrieval (CBIR) to extract the visual features of tongue images and used k-means as classifier. ...
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 extraction networks are trained using softmax and center loss. The experiment results are evaluated using different measures, including overall accuracy, Kappa coefficient, individual sensitivity, etc. The results demonstrate that the proposed framework with SVM achieves up to 97.60% accuracy in the tongue image datasets.
... Like other image classification tasks, TDS requires feature extraction and classification stages as well. In these works [3,4,5], both stages are implemented separately. On the other hand, research in TDS is affected by the environmental factors such as brightness [6]. ...
... The main objective of this paper is to develop a high accuracy TDS based on CNN. This work trains and tests the system using data obtained from these works [5] [13] which consists of 257 labelled tongue images. There are three classes of tongue images in the database (i.e. ...
... On the other hand, Kamarudin et al. proposed a SVM based colour classification TDS [5]. The work uses binary SVM to classify tongue image into deep red or light-red/red. ...
Tongue diagnosis is known as one of the effective and yet noninvasive techniques to evaluate patient’s health condition in traditional oriental medicine such as traditional Chinese medicine and traditional Korean medicine. However, due to ambiguity, practitioners may have different interpretation on the tongue colour, body shape and texture. Thus, research of automatic tongue diagnosis system is needed for aiding practitioners in recognizing the features for tongue diagnosis. In this paper, a tongue diagnosis system based on Convolution Neural Network (CNN) for classifying tongue colours is proposed. The system extracts all relevant information (i.e., features) from three-dimensional digital tongue image and classifies the image into one of the colours (i.e. red or pink). Several pre-processing and data augmentation methods have been carried out and evaluated, which include salt-and-pepper noises, rotations and flips. The proposed system achieves accuracy of up to 88.98% from 5-fold cross validation. Compared to the reported baseline Support Vector Machine (SVM) method, the proposed method using CNN results in roughly 30% improvement in recognition accuracy.
... In addition, studies have demonstrated that accurate detection, identification, and multidimensional quantitative analysis based on tongue data and pulse data have been gradually applied to disease diagnosis. By constructing the diagnostic relationship between tongue and pulse and health status, it not only saves medical resources but also greatly improves diagnosis efficiency and treatment [19][20][21][22]. Qi deficiency syndrome and Yin deficiency syndrome are the two main common syndromes of NSCLC. ...
Objective:
To explore the data characteristics of tongue and pulse of non-small-cell lung cancer with Qi deficiency syndrome and Yin deficiency syndrome, establish syndrome classification model based on data of tongue and pulse by using machine learning methods, and evaluate the feasibility of syndrome classification based on data of tongue and pulse.
Methods:
We collected tongue and pulse of non-small-cell lung cancer patients with Qi deficiency syndrome (n = 163), patients with Yin deficiency syndrome (n = 174), and healthy controls (n = 185) using intelligent tongue diagnosis analysis instrument and pulse diagnosis analysis instrument, respectively. We described the characteristics and examined the correlation of data of tongue and pulse. Four machine learning methods, namely, random forest, logistic regression, support vector machine, and neural network, were used to establish the classification models based on symptom, tongue and pulse, and symptom and tongue and pulse, respectively.
Results:
Significant difference indices of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll, and the tongue coating texture indices including TC-CON, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indices of pulse diagnosis were t4 and t5. The classification performance of each model based on different datasets was as follows: tongue and pulse < symptom < symptom and tongue and pulse. The neural network model had a better classification performance for symptom and tongue and pulse datasets, with an area under the ROC curves and accuracy rate which were 0.9401 and 0.8806.
Conclusions:
It was feasible to use tongue data and pulse data as one of the objective diagnostic basis in Qi deficiency syndrome and Yin deficiency syndrome of non-small-cell lung cancer.
... Visual inspection by practitioner's naked eye can lead to misjudgment since the color information or substances on a tongue might look similar. In order perform a fast, high accuracy classification and minimize the ambiguity or other limitations, a two-stage tongue's multicolor classification using Support Vector Machine (SVM) and k-means clustering has been introduced in this research [9].. Firstly, the pre-classification process starts with k-means clustering algorithm that implemented Lab color space to generate most informative clusters (clustering identifiers) as biomarkers to be fed into SVM. By having pre-classification stage, the computational complexity is greatly reduced since the pre-classification process preserves only most useful and contributing colors information. ...
... Basically, k-means clustering is one of the simplest unsupervised learning algorithms that solved clustering problems and cluster analysis method that aims to divide and partition data points into k clusters in which each data belongs to the cluster (group) with the nearest mean (based on similarity) [9], [21]. This algorithm consists of two separate phases. ...
... Then the process continues with clustering the CIE Lab color space using k-means cluster with the evaluation of Euclidean distance during the first stage of classification. The number of cluster was set to four (k = 4) in order to distinguish the surface area of the tongue body that were determined as background color, transitional pixels/tongue coating, deep red pixels of tongue region and red/light red pixels of tongue region [9]. Clustering method of k-means algorithm to cluster tongue surface into four clusters was using Euclidean distance metric with centroid detection and color pixels. ...
Tongue inspection is a complementary diagnosis method that widely used in Traditional Chinese Medicine (TCM) by inspecting the tongue body constitution to decide the physiological and pathological functions of the human body. Since tongue manifestation is done by practitioner’s observation using naked eye, many limitations can affect the diagnosis result including environment condition and experiences of the practitioner. Lately, tongue diagnosis has been widely studied in order to solve these limitations via digital system. However, most of recent digital system are bulky and not equipped with intelligent diagnosis system that can finally predict the health status of the patient. In this research, digital tongue diagnosis system that uses intelligent diagnosis consisted of image segmentation analysis, tongue coating recognition analysis, and tongue color classification has been embedded on Raspberry Pi. Tongue segmentation implements Hue, Saturation and Value (HSV) color space with Brightness Conformable Multiplier (BCM) for adaptive brightness filtering to recognized tongue body accurately while eliminating perioral area. Tongue Coating Recognition uses threshold method to detect tongue coating and eliminate the unwanted features including shadow. Tongue color classification uses hybrid method consisted of k-means clustering and Support Vector Machine (SVM) to classify between red, light red and deep red tongue and further gave diagnosis based on color. This experiment concluded that it is feasible to embed the algorithm on Raspberry Pi to promote system portability while attaining similar accuracy for future telemedicine.
... J. Zhang [11] also used SVM to establish the classification model for diabetes based on standardized tongue image, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. It indicates the feasibility of using the information science method to carry out TCM diagnosis.Nur Diyana Kamarudin et al. [46]presented a two-stage tongue's multicolor classification based on a SVM whose support vectors were reduced by our proposed k-means clustering identifiers and red color range for precise tongue color diagnosis to overcome this ambiguity in the judgement of tongue. Zhang et al. [45] proposed the BN classifiers based on quantitative chromatic and textural measurements to classified six groups: healthy, pulmonary heart disease, appendicitis, gastritis, pancreatitis and bronchitis. ...
Tongue diagnosis is an important process to non-invasively assess the condition of a patient’s internal organs
in traditional Chinese medicine (TCM) and each part of the tongue is related to corresponding internal organs.
Due to continuing computer technological advances, especially the artificial intelligence (AI) methods have
achieved significant success in tackling tongue image acquisition, processing, and classification, novel AI
methods are being introduced in traditional Chinese medicine tongue diagnosis medical practices. Traditional
tongue diagnose depends on observations of tongue characteristics, such as color, shape, texture, moisture, etc.
by traditional Chinese medicine physicians. The appearance of the tongue color, texture and coating reflects
the improvement or deterioration of patient’s conditions. Moreover, AI can now distinguish patient’s condition
through tongue images, texture or coating, which is all possible increasingly with help from traditional Chinese
medicine physicians under the traditional Chinese medicine tongue theory. AI has enabled humans to do what
was previously unimagined: traditional Chinese medicine tongue diagnosis with feeding a large amount of
tongue image and tongue texture/coating data to train the AI modes. This review focuses on the research
advances of AI in TCM tongue diagnosis thus far to identify the major scientific methods and prospects. In this
article, we tried to review the AI application in resolving the tongue diagnosis of traditional Chinese medicine
on color correction, tongue image extraction, tongue texture/coating segmentation.
Keywords: Artificial intelligence, Traditional Chinese medicine, Tongue diagnosis, Machine learning, Deep
learning, Color model, Tongue segmentation, Tongue image extraction
... To enhance the efficiency, a novel zero-shot learning technique is presented by combining features and learning discriminant latent attributes that could resolve the imbalance challenge of constitution classifications. Kamarudin et al. [21] proposed a 2 phase tongue multicolor classification depending upon SVM that is decreased by this presented k mean clustering detectors and red colour range to diagnose accurate tongue colour. Initially, k-means clustering is utilized for clustering a tongue image to 4 clusters of deep red region, image background (black), transitional region, and red or light red region. ...
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.
... Support vector machine was one of the machine learning algorithms based on supervised method of classification by identifying the hyper planes (Kamarudin et al., 2017). It was a non-parametric classifier suitable for two-class classification problems. ...
The main objectives of this study are (i) to perform automated segmentation of facial regions from thermograms using k-means clustering algorithm and to classify the data into normal and orofacial pain (OFP) categories using various machine learning classi-fiers (ii) to implement the convolutional neural network (CNN) for classification of normal and OFP subjects which involves automated feature extraction and feature selection process. Fifty normal and 50 diseased cases suffering from orofacial pain were included in the study. Facial thermograms were segmented using k-means algorithm , then statistical features were extracted and classified into normal and OFP using various machine learning classifier. Further, the deep learning networks such as VGG-16 and DenseNet-121 were used for automated feature extraction and classification of facial thermograms. The facial temperature variations of 3.46%, 3.4%, and 3.27% were observed in the front, right and left side facial regions respectively between the normal and the OFP subjects. Machine learning classifiers such as support vector machine (SVM) and random forest (RF) classifier provided the highest accuracy of 99%. On the other hand, deep learning models such as modified VGG-16 achieved an average accuracy of 97% compared to modified DenseNet-121 which produced an average accuracy of 68% in classification of normal and OFP thermo-grams. Thus, computer aided diagnosis of facial thermography could be used as a viable screening device for a reliable identification of tooth pathology before the occurrence of structural changes and complications. K E Y W O R D S computer-aided diagnosis, convolution neural network, facial thermogram, feature extraction, k-means algorithm, orofacial pain
