Analyzing Tongue Images Using a Conceptual Alignment Deep Autoencoder

Dai, Yinglong; Wang, Guojun

doi:10.1109/access.2017.2788849

Cited by 30 publications

(8 citation statements)

References 29 publications

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“…Normally, traditional methods are base on hand-designed features and contrast inference mechanisms. The DL techniques require raw image data only [ 109 , 110 , 111 , 112 ]. Also, DL techniques applied to Glaucoma detection with promising results [ 113 , 114 , 115 ].…”

Section: Main Textmentioning

confidence: 99%

State-of-the-art in artificial neural network applications: A survey

Abiodun

Omolara

Dada

et al. 2018

Heliyon

1,844

785

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This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.

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Section: Main Textmentioning

confidence: 99%

State-of-the-art in artificial neural network applications: A survey

Abiodun

Omolara

Dada

et al. 2018

Heliyon

1,844

785

View full text Add to dashboard Cite

show abstract

“…The number of samples belonging to the class V This change of formulation based on the distance of weight is between the pair of sample data so, in the formula of (8) , the values of non-zero weights ,to scattering between non-bordering patterns , by similar label is specified and the values of zero -weight represents the difference between non-bordering patterns [44,[46][47][48][49][50][51][52]. As it is observed, the patterns which are in the non -Boundary region do not have any effect on are in calculating between classes , classes , as well as they separating between the classes because they have been well-separated therefore, the other point is that this reality that based on the criteria of Boundary and non-Boundary patterns , the Boundary patterns between two class that in original space have been well-separated , is classified as non-Boundary pattern and does not have any effect in the estimate of scattering matrix between the classes .…”

Section: Discussing About the Effectiveness Of The Proposing Measurementioning

confidence: 99%

Improve Heteroscedastic Discriminant Analysis by Using CBP Algorithm

Alzubi

Yaghoubi

Gheisari

et al. 2018

Algorithms and Architectures for Parallel Processing

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Linear discriminant analysis is considered as current techniques in feature extraction so, LDA, by discriminant information which obtains in mapping space, does the classification act. When the classes' distribution is not normal, LDA, to perform classification, will face problem and will resulted the poor performance of criteria in performing the classification act. One of the proposed ways is the use of other measures, such as Chernoff's distance so, by using Chernoff's measure LDA has been spreading to its heterogeneous states and LDA in this state, in addition to use information among the medians, uses the information of the classes' Covariance matrices. By defining scattering matrix , based on Boundary and non-Boundary samples and using these matrices in Chernoff's criteria, the decrease of the classes' overlapping in the mapping space in as result, the rate of classification correctness increases. Using Boundary and non-Boundary samples in scattering matrices causes improvement over the result. In this article, we use a new discovering multi-stage Algorithm to choose Boundary and non-Boundary samples so, the results of the conducted experiments shows promising performance of the proposing method.

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“…The strategy of connecting neurons to local receptive fields vastly reduce the amount of model parameters and make the model more efficiency. By utilizing CNN structure combined with CADNN in inspection channels, we can efficiently extract features from high‐dimensional image data and encode image representations 11 …”

Section: Methodsmentioning

confidence: 99%

“…According to traditional diagnostic methods, Wang et al 9 developed a Constitution in Chinese Medicine Questionnaire (CCMQ) to scale nine BC types, as shown in Table . We also provided a perspective on the BC types according to the scores of CCMQ items, 10 and tongue image analysis of BC types 11 …”

Section: Introductionmentioning

confidence: 99%

A multimodal deep architecture for traditional Chinese medicine diagnosis

Dai

Wang

Dai

et al. 2020

Concurrency and Computation

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Traditional Chinese medicine (TCM) is found on a long‐term medical practice in China. Rare human brains can fully grasp the deep TCM knowledge derived from a tremendous amount of experience. In this big data era, a big electronic brain might be competent via deep learning techniques. For this prospect, the electronic brain needs to process various heterogeneous data, such as images, texts, audio signals, and other sensory data. It used to be a challenge to analyze the heterogeneous data by the computer‐aided system until the advances of the powerful deep learning tools. We propose a multimodal deep learning framework to mimic a TCM practitioner to diagnose a patient on the basis of multimodal perceptions of see, listen, smell, ask, and touch. The framework learns common representations from various high‐dimensional sensory data, and fuse the information for final classification. We propose to use conceptual alignment deep neural networks to embed prior knowledge and obtain interpretable latent representations. We implement a multimodal deep architecture to process tongue image and description text data for TCM diagnosis. Experiments illustrate that the multimodal deep architecture can extract effective features from heterogeneous data, produce interpretable representations, and finally achieve a higher accuracy than either corresponding unimodal architectures.

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Analyzing Tongue Images Using a Conceptual Alignment Deep Autoencoder

Cited by 30 publications

References 29 publications

State-of-the-art in artificial neural network applications: A survey

State-of-the-art in artificial neural network applications: A survey

Improve Heteroscedastic Discriminant Analysis by Using CBP Algorithm

A multimodal deep architecture for traditional Chinese medicine diagnosis

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