Deep Neural Networks Approach to Microbial Colony Detection—A Comparative Analysis

We compared the performance of three well-known deep learning approaches for object detection on the AGAR dataset, namely two-stage, one-stage, and transformer-based neural networks.

Self-normalized density map (SNDM) for counting microbiological objects

Based on our investigation, we propose a self-normalization module in the U2-Net. The improved model, called Self-Normalized Density Map (SNDM), can correct its output density map by itself to accurately predict the total number of microbial colonies in the Petri dish image.

Generation of microbial colonies dataset with deep learning style transfer

We introduce an effective strategy to generate an annotated synthetic dataset of microbiological images of Petri dishes that can be used to train deep learning models in a fully supervised fashion.

AGAR a Microbial Colony Dataset for Deep Learning Detection

We introduced the Annotated Germs for Automated Recognition (AGAR) dataset, an image database of microbial colonies cultured on agar plates. It contains 18000 photos of five different microorganisms as single or mixed cultures, taken under diverse lighting conditions with two different cameras.

Recognition and identification of bacterial colonies

The aim of the project is to develop a method to automate the analysis of bacterial colonies on Petri dishes using artificial neural networks and Machine Learning algorithms.