ArticleMicrobiology spectrum2025
Implementation of convolutional neural networks for microbial colony recognition.
Article in Microbiology spectrum, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Overcoming resolution constraints in automated colony counting via a high-performance deep learning framework using SAHI.Scientific reports · 2026Article
- Comparative evaluation of deep learning architectures for microbial colony classification in microbiological imaging.BMC microbiology · 2026Article
- [Advances in the Application of Artificial Intelligence in Clinical Microbiological Testing].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Review
- Article
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Authors and funding
7 authors.
Funding
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 categories, namely gram-negative bacilli, gram-positive cocci,
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