ArticleEnvironmental science and pollution research international2021
A convolutional neural network approach for detection of E. coli bacteria in water.
Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Meat-Borne Bacterial Pathogen Detection: Conventional, Molecular and Emerging AI-Based Strategies.Diagnostics (Basel, Switzerland) · 2026Review
- Dissolved Carbon Dioxide: The Lifespan ofBiology · 2023Article
- Waterborne pathogens detection technologies: advances, challenges, and future perspectives.Frontiers in microbiology · 2023Review
- Clinical screening of Nocardia in sputum smears based on neural networks.Frontiers in cellular and infection microbiology · 2023Article
- Cefsulodin and Vancomycin: A Supplement for Chromogenic Coliform Agar for Detection ofMicroorganisms · 2022Article
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Authors and funding
3 authors.
Funding
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Abstract
The detection of Escherichia coli bacteria is essential to prevent health diseases. According to the laboratory-based methods, 12-48 h is required to detect bacteria in water. The drawback of depending on laboratory-based methods for the detection of E. coli bacteria can be prone to human errors. Hence, the bacterial detection process must be automated to reduce error. We implement an automated E. coli bacteria detection process using convolutional neural network (CNN) to address this issue. We have also proposed a mobile application for the rapid detection of E. coli bacteria in water that uses CNN. The developed CNN model achieved an accuracy of 96% and an error (loss) of 0.10, predicting each sample in only 458ms. The performance of the model was validated using the F-score, precision, sensitivity, and accuracy statistical measures, which shows that the model is reliable and effective in detecting E. coli. The study generates a methodology for predicting E. coli bacteria in water, which can be used to predict hotspots in terms of continuous exposure to water contamination.
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