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.
Funder
National Funds from FCT—Fundação para a Ciência e a Tecnologia
Reference17 articles.
1. Automated counting of bacterial colonies on agar plates based on images captured at near-infrared light;Zhu;J. Microbiol. Methods,2018
2. Automated counting of bacterial colonies by image analysis;Raju;J. Multidiscip. Dent. Res.,2020
3. Breakwell, D.P., Macdonald, B., Woolverton, C.J., Smith, K.C., and Robison, R.A. (2007, January 16–19). Colony Morphology Protocol. Proceedings of the ASM Conference for Undergraduate Educators, San Diego, CA, USA.
4. Rodrigues, P.M., Luís, J., and Tavaria, F.K. (2022). Image Analysis Semi-Automatic System for Colony-Forming-Unit Counting. Bioengineering, 9.
5. Inhibition of Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Enterococcus feacalis through Malus DomesticaExtracts to Eliminate Food Borne Illness;Farooq;Am. J. Biomed. Sci. Res.,2019
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