Automated Identification of Bacteria Using Three-dimensional Holographic Imaging and Convolutional Neural Network

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dc.contributor.authorKim, Geonko
dc.contributor.authorJo, YoungJuko
dc.contributor.authorCho, Hyungjooko
dc.contributor.authorChoi, Gunhoko
dc.contributor.authorKim, Beom-Sooko
dc.contributor.authorMin, Hyun-seokko
dc.contributor.authorPark, YongKeunko
dc.date.accessioned2020-06-23T00:20:16Z-
dc.date.available2020-06-23T00:20:16Z-
dc.date.created2020-06-11-
dc.date.created2020-06-11-
dc.date.issued2018-10-
dc.identifier.citation31st Annual IEEE Photonics Conference (IPC) of the IEEE-Photonics-Society-
dc.identifier.issn2374-0140-
dc.identifier.urihttp://hdl.handle.net/10203/274781-
dc.description.abstractRapid identification of microbial pathogens is crucial for treating infections. Here we present a rapid method for identification of bacteria. In our method, a trained convolutional neural network classifier can accurately determine the bacterial species from a given three-dimensional refractive index image.-
dc.languageEnglish-
dc.publisherIEEE-
dc.titleAutomated Identification of Bacteria Using Three-dimensional Holographic Imaging and Convolutional Neural Network-
dc.typeConference-
dc.identifier.wosid000460542800049-
dc.identifier.scopusid2-s2.0-85058266331-
dc.type.rimsCONF-
dc.citation.publicationname31st Annual IEEE Photonics Conference (IPC) of the IEEE-Photonics-Society-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationReston, VA-
dc.identifier.doi10.1109/IPCon.2018.8527133-
dc.contributor.localauthorPark, YongKeun-
dc.contributor.nonIdAuthorKim, Geon-
dc.contributor.nonIdAuthorJo, YoungJu-
dc.contributor.nonIdAuthorCho, Hyungjoo-
dc.contributor.nonIdAuthorChoi, Gunho-
dc.contributor.nonIdAuthorKim, Beom-Soo-
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PH-Conference Papers(학술회의논문)
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