ArticlePloS one2020
Automated classification of bacterial cell sub-populations with convolutional neural networks.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Novel convolutional neural network for bacterial identification of confocal microscopic datasets.Scientific reports · 2026Article
- Application of few-shot learning and transfer learning based on YOLOv6 in the recognition of bacteria in sputum M-ROSE.Frontiers in cellular and infection microbiology · 2026Article
- Fluorescence-based spectrometric and imaging methods and machine learning analyses for microbiota analysis.Mikrochimica acta · 2025Review
- Effect of shear rate on earlyBiofilm · 2024Article
- Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer.Artificial intelligence review · 2023Article
- A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches.Artificial intelligence review · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Quantification of phenotypic heterogeneity present amongst bacterial cells can be a challenging task. Conventionally, classification and counting of bacteria sub-populations is achieved with manual microscopy, due to the lack of alternative, high-throughput, autonomous approaches. In this work, we apply classification-type convolutional neural networks (cCNN) to classify and enumerate bacterial cell sub-populations (B. subtilis clusters). Here, we demonstrate that the accuracy of the cCNN developed in this study can be as high as 86% when trained on a relatively small dataset (81 images). We also developed a new image preprocessing algorithm, specific to fluorescent microscope images, which increases the amount of training data available for the neural network by 72 times. By summing the classified cells together, the algorithm provides a total cell count which is on parity with manual counting, but is 10.2 times more consistent and 3.8 times faster. Finally, this work presents a complete solution framework for those wishing to learn and implement cCNN in their synthetic biology work.
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Registered trials
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