Application and prospects of hyperspectral imaging and deep learning in traditional Chinese medicine in context of AI and industry 4.0.
10.19540/j.cnki.cjcmm.20200630.603
- Author:
Yi TAO
1
;
Lin CHEN
1
;
En-Ci JIANG
1
;
Ji-Zhong YAN
1
Author Information
1. Group of Intelligent Manufacturing, Institute of Chinese Materia Medica, College of Pharmaceutical Science,Zhejiang University of Technology Hangzhou 310014, China.
- Publication Type:Journal Article
- Keywords:
artificial intelligence;
deep learning;
hyperspectral imaging;
traditional Chinese medicine
- MeSH:
Algorithms;
Artificial Intelligence;
Deep Learning;
Hyperspectral Imaging;
Medicine, Chinese Traditional;
Neural Networks, Computer
- From:
China Journal of Chinese Materia Medica
2020;45(22):5438-5442
- CountryChina
- Language:Chinese
-
Abstract:
In the 21 st century, the rise of artificial intelligence(AI) marks the arrival of the intelligence era or the era of Industry 4.0. In addition to the rapid development of computer and electronic information science, machine learning, as the core intelligence of AI, provides a new methodology for the modernization of traditional Chinese medicine. The algorithms of machine learning include support vector machine(SVM), extreme learning machine(ELM), convolutional neural network(CNN), and recurrent neural network(RNN). The combination of machine learning algorithms and hyperspectral imaging analysis could be used for the identification of fake and inferior herbs, the origin of herbs and the content determination of bioactive ingredients in herbs, which has largely solved the difficulty in strictly controlling the quality of traditional Chinese medicine. The integration of high spectral imaging(HSI) and deep lear-ning will make the predicted results more reliable and suitable for analysis of great amounts of samples. This paper summarizes the application of hyperspectral imaging technology(HSI) and machine learning algorithms in the field of traditional Chinese medicine in recent years, focuses on the principles of hyperspectral imaging technology, preprocessing methods and deep learning algorithms, and gives the prospects of evolution of hyperspectral imaging technology in the field.
