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dc.contributor.authorMajerčík, Jakub
dc.contributor.authorŠpaček, Michal
dc.date.accessioned2021-07-15T13:12:40Z
dc.date.available2021-07-15T13:12:40Z
dc.date.issued2020cs
dc.identifier.citationProceedings II of the 26st Conference STUDENT EEICT 2020: Selected Papers. s. 28-31. ISBN 978-80-214-5868-0cs
dc.identifier.isbn978-80-214-5868-0
dc.identifier.urihttp://hdl.handle.net/11012/200663
dc.description.abstractHuman prostate cancer PC-3 cell line is widely used in cancer research. Previously, Zinc- Resistant variant was described characteristically by higher dry cellular mass determined by quantitative phase imaging. This work aims to classify these 2 cell types into corresponding categories using machine learning methods. We have achieved 97.5% accuracy with the correct preprocessing using Res-Net network.en
dc.formattextcs
dc.format.extent28-31cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 26st Conference STUDENT EEICT 2020: Selected papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/EEICT2020cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.subjectcell classificationen
dc.subjectdeep learningen
dc.subjectneural networken
dc.subjectquantitative phase imagingen
dc.subjectmicroscopyen
dc.titleProstatic Cells Classification Using Deep Learningen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
but.event.date23.04.2020cs
but.event.titleStudent EEICT 2020cs
dc.rights.accessopenAccessen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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