*Communication* **Distinction of Different Colony Types by a Smart-Data-Driven Tool**

**Pedro Miguel Rodrigues \*, Pedro Ribeiro and Freni Kekhasharú Tavaria**

CBQF—Centro de Biotecnologia e Química Fina–Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal **\*** Correspondence: pmrodrigues@ucp.pt

**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 deeplearning models with classical machine-learning models can help to discriminate bacteria colonies with good *accuracy* ratios.

**Keywords:** petri-plates; colonies; machine-learning models; discrimination
