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Deep neural networks outperform human expert's capacity in characterizing bioleaching bacterial biofilm composition
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2019 (English)In: Biotechnology Reports, ISSN 0156-1383, E-ISSN 2215-017X, Vol. 22, article id e00321Article in journal (Refereed) Published
Abstract [en]

Background: Deep neural networks have been successfully applied to diverse fields of computer vision. However, they only outperform human capacities in a few cases. Methods: The ability of deep neural networks versus human experts to classify microscopy images was tested on biofilm colonization patterns formed on sulfide minerals composed of up to three different bioleaching bacterial species attached to chalcopyrite sample particles. Results: A low number of microscopy images per category (<600) was sufficient for highly efficient computational analysis of the biofilm's bacterial composition. The use of deep neural networks reached an accuracy of classification of ∼90% compared to ∼50% for human experts. Conclusions: Deep neural networks outperform human experts’ capacity in characterizing bacterial biofilm composition involved in the degradation of chalcopyrite. This approach provides an alternative to standard, time-consuming biochemical methods. © 2019 The Author

Place, publisher, year, edition, pages
Elsevier B.V. , 2019. Vol. 22, article id e00321
Keywords [en]
Acidophiles, Bacterial biofilm, Biomining, Convolutional neural networks, Deep learning, Microscopy imaging, sulfide, Acidithiobacillus caldus, Article, artificial neural network, bacterium, bacterium culture, biofilm, bioleaching, epifluorescence microscopy, high throughput screening, image analysis, Leptospirillum ferriphilum, microbial colonization, microscopy, nonhuman, performance, priority journal, Sulfobacillus thermosulfidooxidans, training
Identifiers
URN: urn:nbn:se:lnu:diva-86412DOI: 10.1016/j.btre.2019.e00321Scopus ID: 2-s2.0-85063054023OAI: oai:DiVA.org:lnu-86412DiVA, id: diva2:1337036
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Export Date: 22 May 2019; Article

Available from: 2019-07-11 Created: 2019-07-11 Last updated: 2019-07-11

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Dopson, Mark

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf