# Deep Learning to Detect Bacterial Colonies for the Production of Vaccines

arXiv: https://arxiv.org/abs/2009.00926 · HTML: https://mlsanity.com/paper/2009.00926/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [Generation of microbial colonies dataset with deep learning style transfer](https://mlsanity.com/paper/2111.03789/) (2021)
- [Transformer-based Detection of Microorganisms on High-Resolution Petri Dish Images](https://mlsanity.com/paper/2308.09436/) (2023)
- [A Mask R-CNN approach to counting bacterial colony forming units in pharmaceutical development](https://mlsanity.com/paper/2103.05337/) (2021)
- [Self-Normalized Density Map (SNDM) for Counting Microbiological Objects](https://mlsanity.com/paper/2203.09474/) (2022)
- [Beyond Human Performance: A Vision-Language Multi-Agent Approach for Quality Control in Pharmaceutical Manufacturing](https://mlsanity.com/paper/2602.20543/) (2026)

## Abstract (arXiv)

During the development of vaccines, bacterial colony forming units (CFUs) are counted in order to quantify the yield in the fermentation process. This manual task is time-consuming and error-prone. In this work we test multiple segmentation algorithms based on the U-Net CNN architecture and show that these offer robust, automated CFU counting. We show that the multiclass generalisation with a bespoke loss function allows distinguishing virulent and avirulent colonies with acceptable accuracy. While many possibilities are left to explore, our results show the potential of deep learning for separating and classifying bacterial colonies.