TCM complexion automatic sorting method applying superficial layer neural network

Abstract

A TCM (Traditional Chinese Medicine) complexion automatic sorting method applying a superficial layer neural network belongs to the field of computer vision. The designed superficial layer neural network has five layers and adopts three different layer structures including an input layer, a characteristic extraction layer and an output layer. The input layer is composed of a convolution layer and a rectified linear unit (ReLU). The characteristic extraction layer is composed of three layers of network, wherein each of the first two layers is composed of a convolution layer and a ReLU activation function; one of the convolution layer and the ReLU is subjected to batch normalization and a pooling layer is added behind the second ReLU of the characteristic extraction layer; the third layer of the characteristic extraction layer is a whole connection layer connected with a ReLU in a followed manner. The output layer is composed of the whole connection layer and is provided with a followed softmax sorter. The method provided by the invention is distinctively advantaged in sorting precision and has invariance property in distortion such as zooming, translation, rotation and the like, is high in robustness and capable of improving sorting precision effectively. Therefore, the depth learning theory is applied to TCM face diagnose objectivity study.

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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