WLDF: Effective Statistical Shape Feature for Cracked Tongue Recognition

Li, Xiaoqiang; Wang, Dan; Cui, Qing

doi:10.5370/jeet.2017.12.1.420

Cited by 7 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang et al [9] realized the prickles extraction on the green tongue image, with an accuracy of 88.47%. Yet, due to the complex and diverse tongue features, classical image processing methods have some problems, such as space-time consumptive algorithm, difficulties in automated high-throughput processing, and weak migration ability in correlation research [10][11][12], which make the comprehensive analysis of tongue images unavailable.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup

Jiang

et al. 2022

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

Background. Research on intelligent tongue diagnosis is a main direction in the modernization of tongue diagnosis technology. Identification of tongue shape and texture features is a difficult task for tongue diagnosis in traditional Chinese medicine (TCM). This study aimed to explore the application of deep learning techniques in tongue image analyses. Methods. A total of 8676 tongue images were annotated by clinical experts, into seven categories, including the fissured tongue, tooth-marked tongue, stasis tongue, spotted tongue, greasy coating, peeled coating, and rotten coating. Based on the labeled tongue images, the deep learning model faster region-based convolutional neural networks (Faster R-CNN) was utilized to classify tongue images. Four performance indices, i.e., accuracy, recall, precision, and F1-score, were selected to evaluate the model. Also, we applied it to analyze tongue image features of 3601 medical checkup participants in order to explore gender and age factors and the correlations among tongue features in diseases through complex networks. Results. The average accuracy, recall, precision, and F1-score of our model achieved 90.67%, 91.25%, 99.28%, and 95.00%, respectively. Over the tongue images from the medical checkup population, the model Faster R-CNN detected 41.49% fissured tongue images, 37.16% tooth-marked tongue images, 29.66% greasy coating images, 18.66% spotted tongue images, 9.97% stasis tongue images, 3.97% peeled coating images, and 1.22% rotten coating images. There were significant differences in the incidence of the fissured tongue, tooth-marked tongue, spotted tongue, and greasy coating among age and gender. Complex networks revealed that fissured tongue and tooth-marked were closely related to hypertension, dyslipidemia, overweight and nonalcoholic fatty liver disease (NAFLD), and a greasy coating tongue was associated with hypertension and overweight. Conclusion. The model Faster R-CNN shows good performance in the tongue image classification. And we have preliminarily revealed the relationship between tongue features and gender, age, and metabolic diseases in a medical checkup population.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup

Jiang

et al. 2022

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

show abstract

“…In general, CNN architectures can avoid feature selection manually and automatically extract features, which are key elements to enable the intelligent tongue diagnosis system into the TCM clinical practice. Although several previous studies have reported encouraging results using CNN methods to extract tongue image features for tongue color (tongue body and tongue coating) classi cation [16][17][18], tongue image characteristic recognition (tooth-marked tongue [19][20][21], cracked tongue [22,23]) and tongue image segmentation [24][25][26][27][28][29][30], but they usually ignore the quality of tongue images, which is strongly related to the accuracy of TCM diagnosis. Thus, the medical application of deep learning methods to the eld of tongue diagnosis has not achieved much so far.…”

Section: Discussionmentioning

confidence: 99%

Tongue Image Quality Assessment Based on Deep Convolutional Neural Network

Jiang

Yao

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: With the wide application of digital tongue diagnosis instrument, massive tongue images will be produced. Adequate image quality is the prerequisite to ensure accurate tongue image analysis. In the process of tongue image collection, improper operation may lead to many poor-quality images (fogging, underexposure, overexposure, blurred focus, wrong tongue posture, etc.), which seriously affect the image processing and the accuracy of image analysis. However traditional pattern recognition is difficult to evaluate the quality of tongue images by extracting features and manual removal of tongue images with bad quality consumes a lot of labor and has a high error rate. In this research, we utilized a deep convolutional neural network to automatically select bad quality tongue images.Methods: The present study was conducted to identify the most appropriate CNN model for Tongue Image Quality Assessment based on deep CNN. The CNN model was evaluated by using Residual neural network and compared with VGGNet and DenseNet. Evaluation metrics such as accuracy, precision, recall, and F1-score were used for CNN model performance.Results: A detection model is established for tongue image quality control based on deep residual network, with an average accuracy of 99.04%, accuracy of 99.05%, recall of 99.04%, and F1-score of 99.05%, which can be used for quality screening of massive tongue images.Conclusions: Our research findings demonstrate various CNN models in the decision-making process for the selection of tongue image quality assessment and prove that applying deep learning methods, specifically deep CNN, to evaluate bad quality tongue images is feasible.

show abstract

“…Zhang et al [16] compared the performance of the watershed algorithm and the Weber local descriptor in fissure extraction, and discovered that the former performed better; however, Zhang focused only on identifying tagged pixels of fissures with certain thresholds without using an effective classifier. Therefore, Li et al [17] improved Zhang's method using the Otsu threshold and histogram equalization. Finally, an SVM was used to recognize fissured tongue, and the identification accuracy increased.…”

Section: U N C O R R E C T E D P R O O F V E R S I O Nmentioning

confidence: 99%

“…In general, although previous studies have realized significant achievements in the field of fissured tongue recognition, some important issues still need to be explored. Firstly, the sample size of the datasets was relatively small (e.g., less than 1000 for [15][16][17]), which may restrict the generalization of the classification models. Secondly, all methods used handcrafted features, although some minor details were difficult to describe.…”

Section: U N C O R R E C T E D P R O O F V E R S I O Nmentioning

confidence: 99%

Classification of fissured tongue images using deep neural networks

Hu¹,

Yan²,

Jiang³

2022

THC

View full text Add to dashboard Cite

BACKGROUND: Tongue inspection is vital in traditional Chinese medicine. Fissured tongue is an important feature in tongue diagnosis, and primarily corresponds to three Chinese medicine syndromes: syndrome-related hotness, blood deficiency, and insufficiency of the spleen. Diagnosis of the syndrome is significantly affected by the experience of clinicians, and it is difficult for young doctors to perform accurate diagnoses. OBJECTIVE: The syndrome not only depends on the local features based on fissured regions but also on the global features of the whole tongue; therefore, a syndrome diagnosis framework combining the global and local features of a fissured tongue image was developed in the present study to achieve a quantitative and objective diagnosis. METHODS: First, we detected the fissured region of a tongue image using a single-shot multibox detector. Second, we extracted the global and local features from a whole tongue image and a fissured region using TongueNet (developed in-house). Third, we developed a classifier to determine the final syndrome. RESULTS: Based on an experiment involving 721 fissured tongue images, we discovered that TongueNet affords better feature extraction. The accuracy of TongueNet was 4% (p< 0.05) and 3% (p< 0.05) higher than that of InceptionV3 and ResNet18, respectively, for whole tongue images. Meanwhile, at local fissured regions, the accuracy of TongueNet was 3% (p< 0.05) higher than that of InceptionV3 and equal to that of ResNet18. Finally, the fusion features outperformed the global and local features with a 78% accuracy. CONCLUSIONS: Our findings indicate that TongueNet designed with batch normalization and dropout is more suitable for uncomplicated images than InceptionV3 and ResNet18. In addition, compared with the global features, the fusion features supplement the detailed information of the fissures and improve classification accuracy.

show abstract

WLDF: Effective Statistical Shape Feature for Cracked Tongue Recognition

Cited by 7 publications

References 20 publications

Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup

Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup

Tongue Image Quality Assessment Based on Deep Convolutional Neural Network

Classification of fissured tongue images using deep neural networks

Contact Info

Product

Resources

About