Deep learning approach to bacterial colony classification.
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 diagnost...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5599001?pdf=render |
| _version_ | 1818034527823462400 |
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| author | Bartosz Zieliński Anna Plichta Krzysztof Misztal Przemysław Spurek Monika Brzychczy-Włoch Dorota Ochońska |
| author_facet | Bartosz Zieliński Anna Plichta Krzysztof Misztal Przemysław Spurek Monika Brzychczy-Włoch Dorota Ochońska |
| author_sort | Bartosz Zieliński |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T06:40:35Z |
| format | Article |
| id | doaj.art-c6fc2f4af0d24efa8995ccf74d8cddcb |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T06:40:35Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-c6fc2f4af0d24efa8995ccf74d8cddcb2022-12-22T01:58:47ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01129e018455410.1371/journal.pone.0184554Deep learning approach to bacterial colony classification.Bartosz ZielińskiAnna PlichtaKrzysztof MisztalPrzemysław SpurekMonika Brzychczy-WłochDorota OchońskaIn 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.http://europepmc.org/articles/PMC5599001?pdf=render |
| spellingShingle | Bartosz Zieliński Anna Plichta Krzysztof Misztal Przemysław Spurek Monika Brzychczy-Włoch Dorota Ochońska Deep learning approach to bacterial colony classification. PLoS ONE |
| title | Deep learning approach to bacterial colony classification. |
| title_full | Deep learning approach to bacterial colony classification. |
| title_fullStr | Deep learning approach to bacterial colony classification. |
| title_full_unstemmed | Deep learning approach to bacterial colony classification. |
| title_short | Deep learning approach to bacterial colony classification. |
| title_sort | deep learning approach to bacterial colony classification |
| url | http://europepmc.org/articles/PMC5599001?pdf=render |
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