In Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE : Tongue diagnosis is one of the characteristics of traditional Chinese medicine (TCM), but traditional tongue diagnosis is affected by many factors, and its differential diagnosis results are not widely recognized. The appearance of tongue diagnosis instruments is the product of the modernization of tongue diagnosis, and it has standard and objective advantages in clinical practice. In this study, based on standard tongue images, a tongue image dataset and detection model were constructed. And based on the deep learning convolutional neural network (CNN) algorithm and visual question answering technology, a human-computer interaction intelligent health detector for tongue image recognition is constructed.
METHODS : In this research, 1420 tongue images were collected. After screening, experts judged them, and annotated the tongue images to form tongue image datasets. Then the artificial intelligence network framework based on deep learning convolutional neural network (CNN), that is, ResNet34, is applied to this dataset to automatically extract image features and realize tongue images classification. Finally, the VGG16 network framework is applied to the dataset to compare the classification model and compare with the classification effect.
RESULTS : In this paper, relevant datasets were formed by collating the tongue images collected by annotation, which verified that the ResNet34 architecture could better perform the task of tooth mark and tongue feature recognition. Compared with similar learning tasks in existing studies, the accuracy of the teeth-printed tongue recognition model proposed in this study is more than 10% higher, which indicates that the CNN algorithm can distinguish teeth-printed tongue more accurately and effectively. At the same time, using datasets and models combined with visual question and answer technology, an AI health detector for TCM tongue image identification is designed, which can make health assessments and give suggestions to users.
CONCLUSION : This study adopts a convolutional neural network model based on deep learning, which can reduce the extraction of tongue features more quickly and conveniently. At the same time, the model architecture has excellent performance and strong generalization ability and is more accurate in judging users' health status.
Zhuang Qingbin, Gan Senzhong, Zhang Liangyu
Convolutional neural network, Deep learning, Health detector, Human-computer interaction, ResNet34, Tongue image