Efficient Detection of Longitudinal Bacteria Fission Using Transfer Learning in Deep Neural Networks.
bacteria classification
bacteria division
deep learning
image processing
image segmentation
longitudinal bacterial fission
transfer learning
Journal
Frontiers in microbiology
ISSN: 1664-302X
Titre abrégé: Front Microbiol
Pays: Switzerland
ID NLM: 101548977
Informations de publication
Date de publication:
2021
2021
Historique:
received:
04
01
2021
accepted:
12
04
2021
entrez:
25
6
2021
pubmed:
26
6
2021
medline:
26
6
2021
Statut:
epublish
Résumé
A very common way to classify bacteria is through microscopic images. Microscopic cell counting is a widely used technique to measure microbial growth. To date, fully automated methodologies are available for accurate and fast measurements; yet for bacteria dividing longitudinally, as in the case of
Identifiants
pubmed: 34168623
doi: 10.3389/fmicb.2021.645972
pmc: PMC8217615
doi:
Types de publication
Journal Article
Langues
eng
Pagination
645972Informations de copyright
Copyright © 2021 Garcia-Perez, Ito, Geijo, Feldbauer, Schreiber and zu Castell.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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