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DoggyTongue: Automate Tongue Segmentation for TCM Diagnosis
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Tongue diagnosis is a useful process in traditional Chinese medicine to assess diseases non-invasively by visually inspecting the tongue and its various properties. In this study, we developed an automated tongue diagnosis system with a mobile app for the general public. The image-segmentation component extracts the tongue body image from an input photograph taken by a smartphone. The tongue-coating color classification component predicts the category of the coating color. The segmented image and diagnosis results are returned to the app and shown to the user. Experimental results show that Mask R-CNN is the optimal choice for tongue-image segmentation under various input image conditions based on the mean interaction over union value of 91% and the Dice score of 95%. ResNeXt outperformed other baseline tongue-coating color classification models. In addition, when the input image is adjusted with our color-correction modules in advance, the classification accuracy of ResNeXt101 is improved by approximately 12%.
Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine (TCM). We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in TCM. We analyze the reasoning path patterns from symptom to syndromes on the knowledge graph. There are two kinds of path patterns in the knowledge graph: one-hop and two-hop. The one-hop path pattern maps the symptom to syndromes immediately. The two-hop path pattern maps the symptom to syndromes through the nature of disease, etiology, and pathomechanism to support the diagnostic reasoning. Considering the different support strengths for the knowledge paths in reasoning, we design a dynamic weight mechanism. We utilize Naïve Bayes and TF-IDF to implement the reasoning method and the weighted score calculation. The proposed method reasons the syndrome results by calculating the possibility according to the weighted score of the path in the knowledge graph based on the reasoning path patterns. We evaluate the method with clinical records and clinical practice in hospitals. The preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in practice. Meanwhile, the method is robust and explainable under the guide of the knowledge graph. It could help TCM physicians, especially primary physicians in rural areas, and provide clinical decision support in clinical practice.
Lung cancer is one of the deadliest cancers all over the world. One of the indications of lung cancers is the presence of the lung nodules which can appear individually or attached to the lung walls. The early detection of these nodules is crucial for saving the patient’s lives. Machine learning and image processing techniques, generally embedded in computer-aided diagnosis (CAD) systems, might help radiologists locate and assess the risk of these nodules. Accordingly, in this paper, we present a framework for identifying pulmonary nodules in lung CT images and a convolutional neural network (CNN) approach to automatically extract the features from lung images, followed by classifying the suspicious regions as either nodule or non-nodule objects. The proposed model is based on Le-Net architectural stylization and the light model is obtained after going through the innovative steps. A subset of LIDC public dataset including N = 7072 CT slices of varying nodule sizes (1 mm to 5 mm) is used to train and validate this approach. The proposed framework carries out all stages of lung segmentation as well as diagnosis and categorization of the existing nodules automatically. Training and validation steps of this network with configurations 2.4GHz Core i5 processor, 8GB memory, and Intel Graphics 520 are performed approximately in six hours and this system yields the performance with accuracy = 90.1%, sensitivity = 84.1%, specificity =91.7%, for identifying the nodules. Compared to other famous CNN architectures, the proposed model is agile (light and fast) and has appropriate performance, thereby is suitable for real-time medical image analysis.
Extracting medical entity relations from Traditional Chinese Medicine (TCM) related article is crucial to connect domain knowledge between TCM with modern medicine. Herb accounts for the majority of Traditional Chinese Medicine, so our work mainly focuses on herb. The problem would be effectively solved by extracting herb-related entity relations from PubMed literature. In order to realize the entity relation mining, we propose a novel deep-learning model with improved layers without manual feature engineering. We design a new segment attention mechanism based on Convolutional Neural Network, which enables extracting local semantic features through word embedding. Then we classify the relations by connecting different embedding features. We first test this method on the Chemical-Induced Disease task and the experiment show better result comparing to other state-of-the-art deep learning methods. Further, we apply this method to a herbal-related data set (Herbal-Disease and Herbal Chemistry, HD-HC) constructed from PubMed to explore entity relation classification. The experiment shows superior results than other baseline methods.
The standardization and digitalization of nine constitutions of traditional Chinese medicine (TCM) have promoted the development of TCM automation. The keys to constitution identification are tongue diagnosis and consultation diagnosis. In this paper, a tongue detection method based on the combination of the histogram of oriented gradients (HOG) and the support vector machine (SVM) is proposed. To separate the tongue body and tongue coating, a k-means segmentation method based on the Lab color space is proposed. Based on the clustering analysis of the difference between the color components of the tongue body and tongue coating in the Lab color space, the separation is realized. Thus, the relationship between the tongue image and constitution can be analyzed. The method for consultation diagnosis is divided into a questionnaire scale and a question answering system. The questionnaire includes 29 quantified items with corresponding weights and answer scores, so the final score of each type of constitution is calculated. The last constitution type is determined on the basis of the scores of both diagnoses. In addition, the key of question answering system is text similarity calculation. First, a sentence similarity calculation method combining the n-gram language model and word2vec is proposed. Then, a multifeature fusion sentence similarity calculation method combining the Word Mover’s Distance (WMD) and editing distance is proposed. Finally, the experimental results show the effectiveness of the proposed method, and a constitution identification application is developed based on the content of this paper.
Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variability. From the perspective of image analysis, it is essential to develop advanced automatic US
image analysis methods to assist in US diagnosis and/or to make such assessment more objective and accurate. Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. Deep learning also shows huge potential for various automatic US image analysis tasks. This review first briefly introduces several popular deep learning architectures, and then summarizes and thoroughly discusses their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation. Finally, the open challenges and potential trends of the future application of deep learning in medical US image analysis are discussed.
Objective . The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM). Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results . After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC) were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA), while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA), the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM) is of great value, indicating the feasibility of digitalized tongue diagnosis.
In Traditional Chinese Medicine (TCM), tongue diagnosis is essential for symptom differentiation and treatment selection. Compared with traditional tongue diagnostic instruments, deploying a tongue diagnosis system on mobile devices is more convenient to monitor general health and facilitates the development of telemedicine. However, limited by both the quality and quantity of tongue images taken by mobile devices, extracting tongue features of the images maintains a great challenge. In this paper, we present a tongue feature extraction method on mobile devices, containing a high-accuracy tongue segmentation method based on Moment Invariants with Data Augmentation (DAMI) and an efficient and lightweight feature classification model with an attention mechanism. Meanwhile, we construct a novel tongue image dataset from mobile devices for extracting tongue features, significantly, first including sublingual images which are beneficial to extracting sublingual vein features. Extensive experiments on two datasets demonstrate the effectiveness and robustness of our method. Furthermore, our method greatly reduces the computing and storage demands compared with other current methods, providing a good prerequisite for deployment on mobile devices. Finally, to demonstrate the potential application of our proposed method, we develop a TCM intelligence tongue diagnosis application, which can be accessed through the WeChat Mini Program or web version, exhibiting its great potential in clinical diagnosis and health monitoring.
Background and Objective
Traditional Chinese Medicine (TCM) diagnosis is based on the theoretical principles and knowledge, where it is steeped in thousands of years of history to diagnose various types of diseases and syndromes. It can be generally divided into four main diagnostic approaches: 1. inspection, 2. auscultation and olfaction, 3. inquiry, and 4. palpation, which are widely used in TCM hospitals in China and around the world. With the development of intelligent computing technology in recent years, computational TCM diagnosis has grown rapidly.
Methods
In this paper, we aim to systematically summarize the development of computational TCM diagnosis based on four diagnostic approaches, mainly focusing on digital acquisition devices, collected datasets, and computational detection approaches (algorithms). Furthermore, all related works of this field are compared and explored in detail.
Results
This survey provides the principles, applications, and current progress in computing for readers and researchers in terms of computational TCM diagnosis. Moreover, the future development direction, prospect, and technological trend of computational TCM diagnosis will also be discussed in this study.
Conclusions
Recent computational TCM diagnosis works are compared in detail to show the pros/cons, where we provide some meaningful suggestions and opinions on the future research approaches in this area. This work is useful for disease detection in computational TCM diagnosis as well as health management in the smart healthcare area.
Index Terms
Computational diagnosis, Traditional Chinese Medicine, survey, smart healthcare.
Objectives:
To develop and compare several machine learning models to predict occult cervical lymph node (LN) metastasis in early-stage oral tongue squamous cell cancer (OTSCC) from preoperative MRI texture features.
Materials and methods:
We retrospectively enrolled 116 patients with early-stage OTSCC (cT1-2N0) who had been surgically treated by tumor excision and elective neck dissection (END). For each patient, we extracted 86 texture features from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (ceT1WI), respectively. Dimension reduction was performed in three consecutive steps: reproducibility analysis, collinearity analysis, and information gain algorithm. Models were created using six machine learning methods, including logistic regression (LR), random forest (RF), naïve Bayes (NB), support vector machine (SVM), AdaBoost, and neural network (NN). Their performance was assessed using tenfold cross-validation.
Results:
Occult LN metastasis was pathologically detected in 42.2% (49/116) of the patients. No significant association was identified between node status and patients' gender, age, or clinical T stage. Dimension reduction steps selected 6 texture features. The NB model gave the best overall performance, which correctly classified the nodal status in 74.1% (86/116) of the carcinomas, with an AUC of 0.802.
Conclusion:
Machine learning-based MRI texture analysis offers a feasible tool for preoperative prediction of occult cervical node metastasis in early-stage OTSCC.
Key points:
• A machine learning-based MRI texture analysis approach was adopted to predict occult cervical node metastasis in early-stage OTSCC with no evidence of node involvement on conventional images. • Six texture features from T2WI and ceT1WI of preoperative MRI were selected to construct the predictive model. • After comparing six machine learning methods, naïve Bayes (NB) achieved the best performance by correctly identifying the node status in 74.1% of the patients, using tenfold cross-validation.
Image classification problem is one of most important research directions in image processing and has become the focus of research in many years due to its diversity and complexity of image information. In view of the existing image classification models’ failure to fully utilize the information of images, this paper proposes a novel image classification method of combining the Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost), which are two outstanding classifiers. The presented CNN-XGBoost model provides more precise output by integrating CNN as a trainable feature extractor to automatically obtain features from input and XGBoost as a recognizer in the top level of the network to produce results. Experiments are implemented on the well-known MNIST and CIFAR-10 databases. The results prove that the new method performs better compared with other methods on the same databases, which verify the effectiveness of the proposed method in image classification problem.
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