CRANet: Tongue Image Segmentation and Classification Network

In Traditional Chinese Medicine (TCM) diagnosis, tongue diagnosis is an important method. However, the accuracy and efficiency of tongue analysis are greatly influenced by subjective factors. This paper proposes a tongue segmentation and classification model based on Convolutional Neural Networks and Attention Mechanism (CRANet), and designs a Convolutional Residual Block (CR Block). The model can automatically segment the tongue and recognize features, thus classifying tongue images and improving the objectivity and accuracy of tongue diagnosis. The proposed model achieved an accuracy of 99.43%, an Intersection over Union (IoU) of 97.50%, and a Dice coefficient of 98.73% in the segmentation task. In the classification task, it achieved an accuracy of 88.91%, a precision of 86.37%, a recall of 85.32%, and an F1 score of 85.84%. This verifies the application potential of the tongue image segmentation and classification method based on Convolutional Neural Networks and Attention Mechanism in TCM tongue diagnosis.