Tongue color classification serves as important assistance for traditional Chinese medicine (TCM) doctors to make a precise diagnosis.This paper proposes a novel two-step framework based on deep learning to improve the Toilet Aids performance of tongue color classification.First, a semantic-based CNN called SegTongue is applied to segment the tongues from the background.Based on DeepLabv3+, multiple atrous spatial pyramid pooling (ASPP) modules are added, and the number of iterations of fusions of low-level and high-level information is increased.After segmentation, various classical feature extraction networks are trained using softmax All tack and center loss.
The experiment results are evaluated using different measures, including overall accuracy, Kappa coefficient, individual sensitivity, etc.The results demonstrate that the proposed framework with SVM achieves up to 97.60% accuracy in the tongue image datasets.