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

Colony morphology (size, color, edge, elevation, and texture), as observed on culture media, can be used to visually discriminate different microorganisms.

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.

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.

These results show that combining deep-learning models with classical machine-learning models can help to discriminate bacteria colonies with good accuracy ratios.