Implementation of convolutional neural networks for microbial colony recognition.

ABSTRACT: Initial classification of microorganisms based on visual identification of colonies remains challenging for skilled microbiologists and is influenced by the proficiency and subjective interpretation of professionals. To overcome these challenges, we applied deep learning to microbial colony recognition to provide microbial data to microbiologists to assist in clinical classification. Photographs of clinically isolated microbial colonies were captured to produce a 48 × 48 pixel colony data set, which was divided into training, validation, and test data sets. Eight convolutional neural networks (CNNs) were adapted to the colony classification task. The classification performance of the models was evaluated based on accuracy, precision, recall, and F1 score. The data set included five categor

SUBMITTER: Kong F 

PROVIDER: S-EPMC12403611 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature