An iterative transfer learning framework for cross‐domain tongue segmentation

Li, Lei; Luo, Zhiming; Zhang, Mengting; Cai, Yuanzheng; Li, Candong; Li, Shaozi

doi:10.1002/cpe.5714

Cited by 17 publications

(12 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Intelligent diagnosis based on images is a main direction of modernization of tongue diagnosis technology [13]. As the current mainstream technology, a convolutional neural network (CNN) has a powerful capability of feature extraction and representation [14,15], which greatly improves the accuracy and efficiency of tongue image segmentation, and classification [16][17][18][19][20]. For example, Chen's team has utilized the deep residual neural network (ResNet) to identify the tooth-marked tongue, with an accuracy of over 90% [21].…”

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

“…Deep-learning-based tongue image segmentation models are mainly divided into two categories, 82 one is U-Net 83,84 and Seg-Net 85,86 evolved from the fully convolutional network (FCN), and the other is Mask R-CNN improved from CNN, 87 which are widely used in various types of medical image segmentation. FCN, the pioneer of semantic segmentation, achieves pixel-level classification of images.…”

Section: Deep-learning-based Tongue Segmentation Methodsmentioning

confidence: 99%

Research and application of tongue and face diagnosis based on deep learning

Huang

Zhong

et al. 2022

DIGITAL HEALTH

View full text Add to dashboard Cite

Objective To explore the technical research and application characteristics of deep learning in tongue-facial diagnosis. Methods Through summarizing the merits and demerits of current image processing techniques used in the traditional medical tongue and face diagnosis, the research status of deep learning in tongue image preprocessing, segmentation, and classification was analyzed and reviewed, and the algorithm was compared and verified with the real tongue and face image. Images of the face and tongue used for diagnosis in conventional medicine were systematically reviewed, from acquisition and pre-processing to segmentation, classification, algorithm comparison, result from analysis, and application. Results Deep learning improved the speed and accuracy of tongue and face diagnostic image data processing. Among them, the average intersection ratio of U-net and Seg-net models exceeded 0.98, and the segmentation speed ranged from 54 to 58 ms. Conclusion There is no unified standard for lingual-facial diagnosis objectification in terms of image acquisition conditions and image processing methods, thus further research is indispensable. It is feasible to use the images acquired by mobile in the field of medical image analysis by reducing the influence of environmental and other factors on the quality of lingual-facial diagnosis images and improving the efficiency of image processing.

show abstract

An iterative transfer learning framework for cross‐domain tongue segmentation

Cited by 17 publications

References 32 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

Research and application of tongue and face diagnosis based on deep learning

Contact Info

Product

Resources

About