Chinese-medicinal-material identification method based on deep neural networks

Abstract

The invention discloses a Chinese-medicinal-material identification method based on deep neural networks. The method includes the following steps: using Chinese-medicinal-material pictures, which arecollected by a web crawler and artificial photographing, as input of a data set, and carrying out preprocessing; and adopting a Bagging method of ensemble learning for training and prediction processes, namely adopting a random sampling method to generate multiple sub-training-sets, utilizing classical convolutional neural network models and all the sub-training-sets to carry out fine-tuning training to generate multiple weak classifiers, wherein the adopted convolutional neural network models include AlexNet, SqueezeNet and GoogleNet, and finally cooperate with a Softmax classification algorithm, and using an ensemble-learning combination strategy to obtain a strong classifier to obtain a classification result, wherein a voting method is adopted for the ensemble-learning combination strategy. The method of the invention is used for auxiliary identification of Chinese medicinal materials, reduces amateur errors appearing in identification, and can analyze the Chinese medicinal materials in a manner of high accuracy, fast identification speed and stable performance.

Description

technical field [0001] The invention relates to the technical field of applying image recognition and integrated learning to the identification of Chinese herbal medicines, in particular to a method for identifying Chinese herbal medicines based on a deep neural network. Background technique [0002] Traditional Chinese medicine takes yin and yang and five elements as the theoretical basis, and regards the human body as a unity of qi, shape, and spirit. Through the method of "seeing, hearing, asking, and feeling", the four diagnosis methods are used to explore the etiology, nature, location, pathogenesis, and internal organs of the human body. , meridians, joints, changes in qi, blood and body fluids, judging the growth and decline of evil, positive and negative, and then get the name of the disease, summarize the disease type, and use the principle of dialectical treatment to formulate "sweating, vomiting, descending, harmonizing, warming, clearing, nourishing, and eliminati...

AI Technical Summary

Problems solved by technology

[0007] However, due to the particularity of the application, applications such as face recognition, emotion recognition, car model recognition and plant recognition based on deep learning are difficult to be extended to Chinese herbal medicine recognition.

Images