Distinction of different colony types by a smart-data-driven tool

Pedro Miguel Rodrigues*, Pedro Ribeiro, Freni Kekhasharú Tavaria

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Background: Colony morphology (size, color, edge, elevation, and texture), as observed on culture media, can be used to visually discriminate different microorganisms. Methods: This work introduces a hybrid method that combines standard pre-trained CNN keras models and classical machine-learning models for supporting colonies discrimination, developed in Petri-plates. In order to test and validate the system, images of three bacterial species (Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus) cultured in Petri plates were used. Results: The system demonstrated the following Accuracy discrimination rates between pairs of study groups: 92% for Pseudomonas aeruginosa vs. Staphylococcus aureus, 91% for Escherichia coli vs. Staphylococcus aureus and 84% Escherichia coli vs. Pseudomonas aeruginosa. Conclusions: These results show that combining deep-learning models with classical machine-learning models can help to discriminate bacteria colonies with good accuracy ratios.
Original languageEnglish
Article number26
Number of pages8
JournalBioengineering
Volume10
Issue number1
DOIs
Publication statusPublished - 24 Dec 2022

Keywords

  • Petri-plates
  • Colonies
  • Machine-learning models
  • Discrimination

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