Deep neural networks approach to microbial colony detection -- a comparative analysis

TL;DR

This paper compares three deep learning methods for microbial colony detection on the AGAR dataset, providing a benchmark to standardize future research in automated microbiology analysis.

Contribution

It offers a comparative analysis of two-stage, one-stage, and transformer-based neural networks for microbial colony detection, addressing the lack of standardized methodology.

Findings

Transformer-based models achieved the highest detection accuracy.

Two-stage detectors performed better in complex colony scenarios.

The study establishes a benchmark for future microbial detection research.

Abstract

Counting microbial colonies is a fundamental task in microbiology and has many applications in numerous industry branches. Despite this, current studies towards automatic microbial counting using artificial intelligence are hardly comparable due to the lack of unified methodology and the availability of large datasets. The recently introduced AGAR dataset is the answer to the second need, but the research carried out is still not exhaustive. To tackle this problem, 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. The achieved results may serve as a benchmark for future experiments.