Tongue segmentation algorithm for traditional Chinese medicine based on convolutional neural network

2019 ◽  
Author(s):  
Pengzhao Sun ◽  
Xiaoping Yang ◽  
Yuhong Ban
Keyword(s):  
Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Convolutional Neural Network ◽  
Segmentation Algorithm ◽  
Tongue Segmentation

Application of Convolutional Neural Network Algorithm in Diagnosis of Chronic Cough and Tongue in Children with Traditional Chinese Medicine

2020 ◽  
Vol 10 (2) ◽  
pp. 401-409
Author(s):  
Zhengke Xiang ◽  
Min Tang ◽  
Huan Yang ◽  
Congrong Tan
Keyword(s):  
Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Convolutional Neural Network ◽  
Clinical Syndrome ◽  
Syndrome Differentiation ◽  
Healthy Children ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Objective Basis

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 syndrome differentiation of children with cough. Methods: The tongue images of children with cough and healthy children who met the inclusion criteria were collected and analyzed using DS01-B TCM tongue imager in the absence of interference factors such as diet and scraping. Images of tongue images on the 1st, 3rd, 5th, 7th, and 10th day from the observation date were collected, and the clinical symptoms, signs, TCM syndromes, mycoplasma antibodies, and blood routine results were recorded. The convolutional neural network algorithm is used to process the data level, including data deletion and tongue segmentation. Results: A total of 134 children with cough were collected as cough group and 30 healthy children were used as control group. The severity of the disease, the course of the disease, whether it is infected with cough and mycoplasma are closely related, and have nothing to do with blood routine. Conclusion: This study used DS01-B Chinese medicine tongue image instrument to collect and treat children's cough tongue image. The convolutional neural network algorithm was used to analyze the tongue image, which made the tongue image result more objective and provided an objective basis for clinical syndrome differentiation of children with cough.

Segmentation Neural Network Incorporating Scale-Space in the Application of Cardiac MRI

2020 ◽  
Vol 10 (7) ◽  
pp. 1494-1505
Author(s):  
Hyo-Hun Kim ◽  
Byung-Woo Hong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cardiac Mri ◽  
Network Architecture ◽  
Data Augmentation ◽  
Scale Space ◽  
Segmentation Algorithm ◽  
Training Data ◽  
Training Procedure ◽  
Whole Heart

In this work, we present an image segmentation algorithm based on the convolutional neural network framework where the scale space theory is incorporated in the course of training procedure. The construction of data augmentation is designed to apply the scale space to the training data in order to effectively deal with the variability of regions of interest in geometry and appearance such as shape and contrast. The proposed data augmentation algorithm via scale space is aimed to improve invariant features with respect to both geometry and appearance by taking into consideration of their diffusion process. We develop a segmentation algorithm based on the convolutional neural network framework where the network architecture consists of encoding and decoding substructures in combination with the data augmentation scheme via the scale space induced by the heat equation. The quantitative analysis using the cardiac MRI dataset indicates that the proposed algorithm achieves better accuracy in the delineation of the left ventricles, which demonstrates the potential of the algorithm in the application of the whole heart segmentation as a compute-aided diagnosis system for the cardiac diseases.

scholarly journals Efficacy-specific Herbal Group Detection from Traditional Chinese Medicine Prescriptions via Hierarchical Attentive Neural Network Model

2020 ◽  
Author(s):  
Li Chen ◽  
Xinglong Liu ◽  
Siyuan Zhang ◽  
Hong Yi ◽  
Yongmei Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Model ◽  
Neural Network Model ◽  
Experimental Results ◽  
Frequent Patterns ◽  
Two Stage ◽  
Second Stage ◽  
Group Detection

Abstract Background: Mining massive prescriptions in Traditional Chinese Medicine (TCM) accumulated in the lengthy period of several thousand years to discover essential herbal groups for distinct efficacies is of significance for TCM modernization, thus starting to draw attentions recently. However, most existing methods for the task treat herbs with different surface forms orthogonally and determine efficacy-specific herbal groups based on the raw frequencies an herbal group occur in a collection of prescriptions. Such methods entirely overlook the fact that prescriptions in TCM are formed empirically by different people at different historical stages, and thus full of herbs with different surface forms expressing the same material, or even noisy and redundant herbs.Methods: We propose a two-stage approach for efficacy-specific herbal group detection from prescriptions in TCM. For the first stage we devise a hierarchical attentive neural network model to capture essential herbs in a prescription for its efficacy, where herbs are encoded with dense real-valued vectors learned automatically to identify their differences on the semantical level. For the second stage, frequent patterns are mined to discover essential herbal groups for an efficacy from distilled prescriptions obtained in the first stage.Results: We verify the effectiveness of our proposed approach from two aspects, the first one is the ability of the hierarchical attentive neural network model to distill a prescription, and the second one is the accuracy in discovering efficacy-specific herbal groups.Conclusion: The experimental results demonstrate that the hierarchical attentive neural network model is capable to capture herbs in a prescription essential to its efficacy, and the distilled prescriptions significantly could improve the performance of efficacy-specific herbal group detection.

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