Research on Human Acupuncture Point Recognition Based on Convolutional Neural Network
Objective:To recognize human acupoints based on convolutional neural network technology.Methods:FasterRCNN model was constructed for the problem of human acupoint recognition,and a practical WeChat applet was built based on the model.Results:The combined use of Early Stopping strategy and Dropout technology could effectively avoid overfitting.In the process of model training,the Early Stopping policy was triggered by setting a maximum number of iterations and a minimum performance improvement threshold to stop the training in advance.At the same time,Dropout technology could be applied in various layers of the neural network to reduce the complexity of the model and enhance the generalization ability of the model.After continuous parameter adjustment training,the test set map of the acupoint model finally reached about 92%,and the loss function reached convergence after 30 iterations.The model gave full play to the advantages of convolutional neural network,which not only ensured the accuracy of recognition,but also realized real-time performance,and proved to have high accuracy and stability through experiments.Conclusion:The WeChat applet based on convolutional neural network technology enables users to obtain acupoint information and understand acupoint knowledge anytime and anywhere,providing convenient health services for the public.
万方数据
维普
