基于迁移学习的全连接神经网络舌象分类方法

目的 针对深度学习在舌象分类中训练数据量大、训练设备要求高、训练时间长等问题，提出一种基于迁移学习的全连接神经网络小样本舌象分类方法。方法 应用经ImageNet海量数据集训练后的卷积Inception_v3网络提取舌象点、线等有效特征，再使用全连接神经网络对特征进行训练分类，将深度学习网络学习到的图像知识迁移到舌象识别任务中。利用舌象数据集进行训练、测试。结果 与典型舌象分类方法K最近邻（KNN）算法、支持向量机（SVM）算法和卷积神经网络（CNN）深度学习方法相比，本实验使用的两种方法（Inception_v3+2NN和Inception_v3+3NN）具有较高的舌象分类识别率，准确率分别达90.30%和93.98%，且样本训练时间明显缩短。结论 与KNN算法、SVM算法和CNN深度学习方法相比，基于迁移学习的全连接神经网络舌象分类方法可有效提高舌象分类的准确率、缩短网络的训练时间。

Tongue image classification method based on transfer learning and fully connected neural network

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.