Deep learning enabled rapid detection of live bacteria in the presence of food debris.

ABSTRACT: The contamination of food with pathogenic bacteria is a major public health concern, requiring rapid and accurate detection methods. Conventional approaches, such as culture-based or molecular assays, are time-consuming, labor-intensive, and often demand specialized expertise. Here, we developed a deep learning-based strategy for rapid detection and classification of live bacteria using simple white-light microscopic images of microcolonies, even in the presence of morphologically similar food debris. The model, based on ResNet50 with a Region Proposal Network, was trained on Escherichia coli, Listeria monocytogenes, Bacillus subtilis, and debris from chicken, spinach, and cheese. The model trained on bacteria misclassified debris as bacteria (24.2% false positives), whereas the model trai

SUBMITTER: Park HW 

PROVIDER: S-EPMC12728209 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature