An automatic classification method of complexion in traditional Chinese medicine using shallow neural network

Abstract

A method for automatically classifying complexion in traditional Chinese medicine by applying a shallow neural network belongs to the field of computer vision. The designed shallow network has 5 layers in total, using three different layer structures, which are input layer, feature extraction layer, and output layer. The input layer consists of a convolutional layer and a rectified linear unit; the feature extraction layer consists of a 3-layer network, each of the first two layers consists of a convolutional layer and a ReLU activation function, between the convolutional layer and the ReLU A batch normalization, and add a pooling layer after the second ReLU of the feature extraction layer, the third layer of the feature extraction layer is a fully connected layer, followed by a corrected linear unit ReLU; the output layer consists of a fully connected layer , followed by a softmax classifier. The invention has obvious advantages in classification accuracy, is invariant to distortions such as zooming, translation, and rotation, has strong robustness, can effectively improve classification accuracy, and applies the theory of deep learning to the objectivity research of face-to-face diagnosis in traditional Chinese medicine. .

Description

technical field [0001] The present invention takes the human facial image as the research object, on the basis of comprehensively analyzing the characteristics of the human facial image, and utilizes the latest research achievement in the field of artificial intelligence—deep learning technology, to propose a method for automatic classification of complexion in traditional Chinese medicine by applying a shallow neural network , this method automatically learns the deep features of human facial images to classify complexion, avoids the uncertain factors produced by manual feature selection, and improves the accuracy and robustness of facial complexion classification in traditional Chinese medicine. The invention belongs to the field of computer vision and the field of face-to-face diagnosis of traditional Chinese medicine, and specifically relates to technologies such as deep learning and image processing. Background technique [0002] The main basis of TCM diagnosis is the i...

AI Technical Summary

Problems solved by technology

However, in the field of face-to-face consultation in traditional Chinese medicine, on the one hand, high-quality medical data samples are seriously scarce; on the other hand, in the process of data collection, due to interference from external factors, the number of correctly labeled high-quality complexion data sets obtained is relatively small. It is difficult to meet the needs of deep learning relying on big data-driven learning

That is to say, the existing combination of deep neural network and small data is difficult to train, and cannot be well applied to the classification of complexion in traditional Chinese medicine.

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