Fissured Tongue Image Recognition Based on Support Vector Machine

Wan, Chao; Zhang, Yue; Xia, Chunming; Pan, Qian; Wang, Yiqin

doi:10.1109/cisp-bmei48845.2019.8965785

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Qiu 26 adopted the meta‐learning method to improve tongue recognition accuracy by leveraging a few samples. Wan et al 27 first filtered, enhanced, and segmented the tongue images. Then, LBP, HOG, and haar‐like features were extracted, respectively.…”

Section: Related Workmentioning

confidence: 99%

A weakly supervised tooth‐mark and crack detection method in tongue image

Hui

Lei

et al. 2021

Concurrency and Computation

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Tongue diagnosis is one of the primary clinical diagnostic methods in Traditional Chinese Medicine. Recognizing the tooth-marked tongue and the crackled tongue plays an essential role in evaluating the status of patients. Previous methods mainly focus on identifying whether a tongue image is a tooth-marked tongue (cracked tongue)or not, while cannot provide more details. In this study, we propose a weakly supervised method for training the tooth-mark and crack detection model by leveraging fully bounding-box level annotated and coarse image-level annotated tongue images. The proposed model is extended from the YOLO object detection model, and we add several classification branches for recognizing the tooth-marked tongue and cracked tongue.The classification branch aims to predict the coarse label for both coarse-labeled data and fully annotated data. The detection branch is used to locate the position of tooth marks and cracks from the fully annotated data. Finally, we utilize a multitask loss function for training the model. Experimental results on a challenging tongue image dataset demonstrate the effectiveness of our proposed weakly supervised method.

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Section: Related Workmentioning

confidence: 99%

A weakly supervised tooth‐mark and crack detection method in tongue image

Hui

Lei

et al. 2021

Concurrency and Computation

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“…[15] used SVM for diabetic tongue image recognition. And Wan et al [16] proposed using SVM for the identification of fissured tongue. In our paper, we embed MIL into SVM as a classifier.…”

Section: Introductionmentioning

confidence: 99%

Research on Multiple-Instance Learning for Tongue Coating Classification

et al. 2021

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Tongue coating can provide valuable diagnostic information to reveal the disorder of the internal body. However, tongue coating classification has long been a challenging task in Traditional Chinese Medicine (TCM) due to the fact that tongue coatings are polymorphous, different tongue coatings have different colors, shapes, textures and locations. Most existing analyses utilize handcrafted features extracted from a fixed location, which may lead to inconsistant performance when the size or location of the tongue coating region varies. To solve this problem, this paper proposes a novel paradigm by employing artificial intelligence to feature extraction and classification of tongue coating. It begins with exploiting prior knowledge of rotten-greasy tongue coating to obtain suspected tongue coating patches. Based on the resulting patches, tongue coating features extracted by Convolutional Neural Network (CNN) are used instead of handcrafted features. Moreover, a multiple-instance Support Vector Machine (MI-SVM) which can circumvent the uncertain location problem is applied to tongue coating classification. Experimental results demonstrate that the proposed method outperforms state-of-the-art tongue coating classification methods.

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Arc Fault Risk Evaluation Based on Image Recognition

Xiao

Xiong

et al. 2021

2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2)

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Fissured Tongue Image Recognition Based on Support Vector Machine

Cited by 5 publications

References 12 publications

A weakly supervised tooth‐mark and crack detection method in tongue image

A weakly supervised tooth‐mark and crack detection method in tongue image

Research on Multiple-Instance Learning for Tongue Coating Classification

Arc Fault Risk Evaluation Based on Image Recognition

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