Tongue image classification method based on transfer learning and fully connected neural network
10.16781/j.0258-879x.2018.08.0897
- Author:
Jing-Dong YANG
1
Author Information
1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Convolutional neural network;
Deep learning;
Tongue presentations;
Transfer learning
- From:
Academic Journal of Second Military Medical University
2018;39(8):897-902
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a classification method for small sample tongue images based on transfer learning and fully connected neural network, so as to solve the problems of large amount of data, high requirement of training equipment and long training time of deep learning in the classification of tongue images. Methods Effective features such as tongue points and lines of tongue images were extracted by the convolution Inception_v3 network after training on the massive data set of ImageNet. The above features were classified by the fully connected neural network, and the image knowledge acquired by the deep learning network was transferred to the tongue image recognition task, and then the tongue data set were used to train and test the efficiency of the network. Results Compared with the typical tongue image classification method such as K-nearest neighbor (KNN) algorithm, support vector machine (SVM) algorithm and convolutional neural network (CNN) deep learning method, the two methods (Inception_v3＋2NN and Inception_v3＋3NN) in our experiment had higher classification rates for tongue images, with the accuracy rates being 90.30% and 93.98%, respectively, and had shorter training time for the sample. Conclusion Compared with KNN algorithm, SVM algorithm and CNN deep learning method, the tongue image classification method based on transfer learning and fully connected neural network can effectively improve the accuracy rate of tongue image classification and shorten the training time.
