Rodrigues Pedro Miguel, Ribeiro Pedro, Tavaria Freni Kekhasharú
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.
Bioengineering (Basel). 2022 Dec 24;10(1):26. doi: 10.3390/bioengineering10010026.
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 (, , and ) cultured in Petri plates were used.
The system demonstrated the following Accuracy discrimination rates between pairs of study groups: 92% for vs. , 91% for vs. and 84% vs. .
These results show that combining deep-learning models with classical machine-learning models can help to discriminate bacteria colonies with good accuracy ratios.
在培养基上观察到的菌落形态（大小、颜色、边缘、隆起和质地）可用于视觉区分不同的微生物。
本研究介绍了一种混合方法，该方法结合了标准预训练的卷积神经网络（CNN）keras模型和经典机器学习模型，用于支持培养皿中菌落的区分。为了测试和验证该系统，使用了在培养皿中培养的三种细菌（、和）的图像。
该系统在各研究组之间显示出以下准确率：与相比为92%，与相比为91%，与相比为84%。
这些结果表明，将深度学习模型与经典机器学习模型相结合有助于以良好的准确率区分细菌菌落。
