Machine learning identification of Pseudomonas aeruginosa strains from colony image data.

ABSTRACT: When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have used these morphology cues to classify bacteria and guide more targeted treatment of pathogens. Advances in genome sequencing technology have revolutionized our ability to classify bacterial isolates and while genomic methods are in the ascendancy, morphological characterization of bacterial species has made a resurgence due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer

SUBMITTER: Rattray JB 

PROVIDER: S-EPMC10752536 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature