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Deep learning approach to bacterial colony classification

Deep learning approach to bacterial colony classification

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dc.contributor.author Zieliński, Bartosz [SAP11020213] pl
dc.contributor.author Plichta, Anna [USOS47825] pl
dc.contributor.author Misztal, Krzysztof [SAP14000847] pl
dc.contributor.author Spurek, Przemysław [SAP14004607] pl
dc.contributor.author Brzychczy-Włoch, Monika [SAP20002305] pl
dc.contributor.author Ochońska, Dorota [SAP20008783] pl
dc.date.accessioned 2017-12-04T12:51:50Z
dc.date.available 2017-12-04T12:51:50Z
dc.date.issued 2017 pl
dc.identifier.uri https://ruj.uj.edu.pl/xmlui/handle/item/47130
dc.language eng pl
dc.rights Udzielam licencji. Uznanie autorstwa 3.0 Polska *
dc.rights.uri http://creativecommons.org/licenses/by/3.0/pl/legalcode *
dc.title Deep learning approach to bacterial colony classification pl
dc.type JournalArticle pl
dc.abstract.en In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods can minimize the risk of incorrect recognition. In this article, we apply the state of the art method for texture analysis to classify genera and species of bacteria. This method uses deep Convolutional Neural Networks to obtain image descriptors, which are then encoded and classified with Support Vector Machine or Random Forest. To evaluate this approach and to make it comparable with other approaches, we provide a new dataset of images. DIBaS dataset (Digital Image of Bacterial Species) contains 660 images with 33 different genera and species of bacteria. pl
dc.description.volume 12 pl
dc.description.number 9 pl
dc.identifier.doi 10.1371/journal.pone.0184554 pl
dc.identifier.eissn 1932-6203 pl
dc.title.journal PLoS ONE pl
dc.language.container eng pl
dc.affiliation Wydział Lekarski : Zakład Molekularnej Mikrobiologii Medycznej pl
dc.affiliation Wydział Matematyki i Informatyki : Instytut Informatyki i Matematyki Komputerowej pl
dc.subtype Article pl
dc.identifier.articleid e0184554 pl
dc.rights.original CC-BY; otwarte czasopismo; ostateczna wersja wydawcy; w momencie opublikowania; 0 pl
dc.identifier.project 2015/19/D/ST6/01215; 2016-2019 pl
dc.identifier.project 2015/19/D/ST6/01472; 2016-2019 pl
dc.identifier.project 2012/07/N/ST6/02192; 2012-2017 pl
.pointsMNiSW [2017 A]: 40


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Udzielam licencji. Uznanie autorstwa 3.0 Polska Except where otherwise noted, this item's license is described as Udzielam licencji. Uznanie autorstwa 3.0 Polska