# Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning

arXiv: https://arxiv.org/abs/2001.10695 · HTML: https://mlsanity.com/paper/2001.10695/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2020.

## Built on by (most cited first, 13 of 13)

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- [Biosensors and Machine Learning for Enhanced Detection, Stratification, and Classification of Cells: A Review](https://mlsanity.com/paper/2101.01866/) (2021)
- [Deep Learning-based Detection of Bacterial Swarm Motion Using a Single Image](https://mlsanity.com/paper/2410.15229/) (2024)
- [Assessing Foundation Models for Mold Colony Detection with Limited Training Data](https://mlsanity.com/paper/2510.00561/) (2025)

## Abstract (arXiv)

We present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60 mm diameter agar-plate and analyzes these time-lapsed holograms using deep neural networks for rapid detection of bacterial growth and classification of the corresponding species. The performance of our system was demonstrated by rapid detection of Escherichia coli and total coliform bacteria (i.e., Klebsiella aerogenes and Klebsiella pneumoniae subsp. pneumoniae) in water samples. These results were confirmed against gold-standard culture-based results, shortening the detection time of bacterial growth by >12 h as compared to the Environmental Protection Agency (EPA)-approved analytical methods. Our experiments further confirmed that this method successfully detects 90% of bacterial colonies within 7-10 h (and >95% within 12 h) with a precision of 99.2-100%, and correctly identifies their species in 7.6-12 h with 80% accuracy. Using pre-incubation of samples in growth media, our system achieved a limit of detection (LOD) of ~1 colony forming unit (CFU)/L within 9 h of total test time. This computational bacteria detection and classification platform is highly cost-effective (~0.6 per test) and high-throughput with a scanning speed of 24 cm2/min over the entire plate surface, making it highly suitable for integration with the existing analytical methods currently used for bacteria detection on agar plates. Powered by deep learning, this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time, also automating the identification of colonies, without labeling or the need for an expert.