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Rapid bacteria identification using structured illumination microscopy and machine learning

查看全文 作　　者：Yingchuan [1]He;Weize [2]Xu;Yao [3]Zhi;Rohit [2,3]Tyagi;Zhe [3]Hu;Gang [2,3,4,5]Cao 高影响力作者 机构地区：[1]College of Engineering Huazhong Agricultural University Wuhan 430070,P.R.China;[2]College of Veterinary Medicine Huazhong Agricultural University Wuhan 430070,P.R.China;[3]State Key Laboratory of Agricultural Microbiology Huazhong Agricultural University Wuhan 430070,P.R.China;[4]Bio-Medical Center Huazhong Agricultural University Wuhan 430070,P.R.China;[5]Key Laboratory of Development of Veterinary Diagnostic Products Ministry of Agriculture,College of Veterinary Medicine Huazhong Agricultural University Wuhan 430070,P.R.China高影响力机构 出　　处：《Journal of Innovative Optical Health Sciences》索引2018年第1期,共10页高影响力期刊 基　　金：supported by the National Key Research and Development Program of China(Grant No.2017-YFD0500303);the National Natural Science Foundation of China(Grant Nos.31371106,91640105);the China Agriculture Research System(No.CARS-36);the Huazhong Agricultural University Scienti¯c and Technological Self-innovation Foundation(Program No.52204-13002). 摘　　要：Traditionally,optical microscopy is used to visualize the morphological features of pathogenic bacteria,of which the features are further used for the detection and ident ification of the bacteria.However,due to the resolution limitation of conventional optical microscopy as well as the lack of standard pattern library for bacteria identification,the ffectiveness of this optical microscopy-based method is limited.Here,we reported a pilot study on a combined use of Structured Illumination Microscopy(SIM)with machine learning for rapid bacteria identification.After applying machine learning to the SIM image datasets from three model bacteria(including Escherichia coli,Mycobacterium smegmatis,and Pseudomonas aeruginosa),we obtained a classifcation accuracy of up to 98%.This study points out a promising possibility for rapid bacterial identification by morphological features. 关 键 词：Structured ilumination microscopy bacterial classification principal component analysis support vector machine random forest
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