Abstract: Tongue image analysis is an important topic in the objective and quantitative research and application of computer vision technology in the diagnosis and treatment of Traditional Chinese Medicine(TCM), and its two key steps are tongue segmentation and tongue image classification. Cascade classifier is used to automatically segment the tongue region on the original image, and then the segmented tongue image is deeply trained and learned on GoogLeNet and ResNet. The obtained depth network is used to classify toothmarks, cracks and thickness of tongue coating. 2245 tongue images obtained from specialized TCM medical institutions are used to build a dataset. In the experiments of classifying the three types of tongue images (toothmarks, cracks and thickness tongue coating), this method has better classification performance than the traditional tongue image feature classification methods. The effectiveness of the tongue feature classification method based on deep transfer learning is verified.

Key words: tongue feature classification, cascade classifier, transfer learning, residual network, GoogLeNet