Tongue Images Classification Based on Constrained High Dispersal Network

Meng, Dan; Cao, Guitao; Duan, Ye; Zhu, Muhua; Tu, Liping; Xu, Dong; Xu, Jiatuo

doi:10.1155/2017/7452427

Cited by 35 publications

(25 citation statements)

References 30 publications

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“…In recent years, deep learning technologies have achieved tremendous success in various computer vision tasks such as image classification, object detection and image segmentation [38]- [42]. Many deep learning based methods, especially deep convolution neural networks, have been proposed for road crack detection.…”

Section: ) Deep Learning Methodsmentioning

confidence: 99%

Review of Pavement Defect Detection Methods

2020

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Road pavement cracks detection has been a hot research topic for quite a long time due to the practical importance of crack detection for road maintenance and traffic safety. Many methods have been proposed to solve this problem. This paper reviews the three major types of methods used in road cracks detection: image processing, machine learning and 3D imaging based methods. Image processing algorithms mainly include threshold segmentation, edge detection and region growing methods, which are used to process images and identify crack features. Crack detection based traditional machine learning methods such as neural network and support vector machine still relies on hand-crafted features using image processing techniques. Deep learning methods have fundamentally changed the way of crack detection and greatly improved the detection performance. In this work, we review and compare the deep learning neural networks proposed in crack detection in three ways, classification based, object detection based and segmentation based. We also cover the performance evaluation metrics and the performance of these methods on commonly-used benchmark datasets. With the maturity of 3D technology, crack detection using 3D data is a new line of research and application. We compare the three types of 3D data representations and study the corresponding performance of the deep neural networks for 3D object detection. Traditional and deep learning based crack detection methods using 3D data are also reviewed in detail. INDEX TERMS Crack detection, image processing, deep learning, 3D imaging.

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Section: ) Deep Learning Methodsmentioning

confidence: 99%

Review of Pavement Defect Detection Methods

2020

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“…However, these studies did not provide assessment methods and results in detail [10,11]. In recent years, artificial intelligence (AI) has been actively applied to medical technology, and significant progress has been made with deep learning in image processing, thereby eliminating the need for image processing experts to extract image features manually [12]. Furthermore, transfer learning, a deep learning model that is pretrained using big data sets, can often be easily applied to different big data sets to interpret image categories.…”

Section: Discussionmentioning

confidence: 99%

Tongue fissure visualization by using deep learning – an example of the application of artificial intelligence in traditional medicine

Chang¹,

Wu²,

Lo³

et al. 2020

Preprint

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Background: Traditional Chinese medicine (TCM) describes physiological and pathological changes inside and outside the human body by the application of four methods of diagnosis. One of the four methods, tongue diagnosis, is widely used by TCM physicians, since it allows direct observations that prevent discrepancies in the patient’s history and, as such, provides clinically important, objective evidence. The clinical significance of tongue features has been explored in both TCM and modern medicine. However, TCM physicians may have different interpretations of the features displayed by the same tongue, and therefore intra- and inter-observer agreements are relatively low. If an automated interpretation system could be developed, more consistent results could be obtained, and learning could also be more efficient. This study will apply a recently developed deep learning method to the classification of tongue features, and indicate the regions where the features are located.Methods: A large number of tongue photographs with labeled fissures were used. Transfer learning was conducted using the ImageNet-pretrained ResNet50 model to determine whether tongue fissures were identified on a tongue photograph. Often, the neural network model lacks interpretability, and users cannot understand how the model determines the presence of tongue fissures. Therefore, Gradient-weighted Class Activation Mapping (Grad-CAM) was also applied to directly mark the tongue features on the tongue image. Results: Only 6 epochs were trained in this study and no graphics processing units (GPUs) were used. It took less than 4 minutes for each epoch to be trained. The correct rate for the test set was approximately 70%. After the model training was completed, Grad-CAM was applied to localize tongue fissures in each image. The neural network model not only determined whether tongue fissures existed, but also allowed users to learn about the tongue fissure regions.Conclusions: This study demonstrated how to apply transfer learning using the ImageNet-pretrained ResNet50 model for the identification and localization of tongue fissures and regions. The neural network model built in this study provided interpretability and intuitiveness, (often lacking in general neural network models), and improved the feasibility for clinical application.

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“…For example, Meng et al designed the CHDNet model, which combined deep learning and support vector machine classifiers to extract and classify tongue features [13].…”

Section: Some Studies Have Applied Deep Learning To the Analysis Of Tmentioning

confidence: 99%

Tongue fissure visualization by using deep learning – an example of the application of artificial intelligence in traditional medicine

Chang¹,

Wu²,

Lo³

et al. 2019

Preprint

View full text Add to dashboard Cite

Background Traditional Chinese medicine (TCM) describes physiological and pathological changes inside and outside the human body by the application of four methods of diagnosis. One of the four methods, tongue diagnosis, is widely used by TCM physicians, since it allows direct observations that prevent discrepancies in the patient’s history and, as such, provides clinically important, objective evidence. The clinical significance of tongue features has been explored in both TCM and modern medicine. However, TCM physicians may have different interpretations of the features displayed by the same tongue, and therefore intra- and inter-observer agreements are relatively low. If an automated interpretation system could be developed, more consistent results could be obtained, and learning could also be more efficient. This study will apply a recently developed deep learning method to the classification of tongue features, and indicate the regions where the features are located. Methods A large number of tongue photographs with labeled fissures were used. Transfer learning was conducted using the ImageNet-pretrained ResNet50 model to determine whether tongue fissures were identified on a tongue photograph. Often, the neural network model lacks interpretability, and users cannot understand how the model determines the presence of tongue fissures. Therefore, Gradient-weighted Class Activation Mapping (Grad-CAM) was also applied to directly mark the tongue features on the tongue image. Results Only 6 epochs were trained in this study and no graphics processing units (GPUs) were used. It took less than 4 minutes for each epoch to be trained. The correct rate for the test set was approximately 70%. After the model training was completed, Grad-CAM was applied to localize tongue fissures in each image. The neural network model not only determined whether tongue fissures existed, but also allowed users to learn about the tongue fissure regions. Conclusions This study demonstrated how to apply transfer learning using the ImageNet-pretrained ResNet50 model for the identification and localization of tongue fissures and regions. The neural network model built in this study provided interpretability and intuitiveness, (often lacking in general neural network models), and improved the feasibility for clinical application.

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Tongue Images Classification Based on Constrained High Dispersal Network

Cited by 35 publications

References 30 publications

Review of Pavement Defect Detection Methods

Review of Pavement Defect Detection Methods

Tongue fissure visualization by using deep learning – an example of the application of artificial intelligence in traditional medicine

Tongue fissure visualization by using deep learning – an example of the application of artificial intelligence in traditional medicine

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