Deep belief network-based drug identification using near infrared spectroscopy

作     者：Huihua Yang Baichao Hu Xipeng Pan Shengke Yan Yanchun Feng Xuebo Zhang Lihui Yin Changqin Hu 

作者机构：College of Electronic Engineering and Automation Guilin University of Electronic Technology 1 Jinji RoadGuilin 541004P.R.China A utomation School Beijing University of Posts 8 Telecommunications 10 Xitucheng RoadBeijing 100876P.R.China National Institutes for Food and Drug Control 10 Tiantanxili RoadBeijing 100050P.R.China 

出 版 物：《Journal of Innovative Optical Health Sciences》 (创新光学健康科学杂志（英文）)

年 卷 期：2017年第10卷第2期

页      面：1-10页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

基　　金：the National Natural Science Foundation of China(Grant Nos.21365008 and 61562013) Natural Science.Foundation of Guangxi(Grant No.2013GXNSFBA019279) Innovation Project of GUET Graduate.Education(Grant Nos.GDYCSZ201474 and GDYCSZ201478) 

主　　题：Deep belief networks near infrared spectroscopy drug classification dropout 

摘      要：Near infrared spectroscopy(NIRS)analysis technology,combined with chemometrics,can be effectively used in quick and nondestructive analysis of quality and *** this paper,an effective drug identification method by using deep belief network(DBN)with dropout mecha-nism(dropout-DBN)to model NIRS is introduced,in which dropout is employed to overcome the overfitting problem coming from the small *** paper tests proposed method under datasets of different sizes with the example of near infrared diffuse refectance spectroscopy of erythromycin ethylsuccinate drugs and other drugs,aluminum and nonaluminum ***,it gives experiments to compare the proposed method s performance with back propagation(BP)neural network,support vector machines(SVMs)and sparse denoising auto-encoder(SDAE).The results show that for both binary classification and multi-classification,dropout mechanism can improve the classification accuracy,and dropout-DBN can achieve best classification accuracy in almost all *** is similar to dropout-DBN in the aspects of classification accuracy and algorithm stability,which are higher than that of BP neural network and SVM *** terms of training time,dropout-DBN model is superior to SDAE model,but inferior to BP neural network and SVM ***,dropout-DBN can be used as a modeling tool with effective binary and multi-class classification performance on a spectrum sample set of small size.