From Parametric Rules to Artificial Intelligence: A New Era in Microbial Image Analysis

In the pharmaceutical industry, image analysis plays a crucial role in the field of microbial identification. Traditional parametric algorithms, while effective for tracking microbial growth over time, struggle with differentiating bacterial from mold species due to their rigid predefined rules.

Machine learning, particularly deep learning, offers a powerful alternative by learning complex patterns from large datasets, enabling more accurate and adaptable classification.

The goal here is to explore the limitations of classical algorithms and the advantages of AI-driven approaches. A case study on automated mold identification on petri dishes will illustrate these concepts in a real-world application.