[1F5-GS-10] AI application:

[1F5-GS-10-02] Study on a microbial identification system using ResNet in deep learning

〇TAKAO NAITO¹, Satomi Takei¹, Miyuki Kuribara¹, Mariko Murakami¹, Shigeki Misawa¹, Kanae Teramoto², Yoko Tabe¹ (1. Juntendo university, 2. Shimadzu corporation )

Keywords:Microbial Identification, Deep Learning, ResNet, Image Recognition

In clinical laboratories, accurate and timely identification of microorganisms is essential. To support the traditional microbial identification, we developed an automated identification system using colony images based on machine learning. For the dataset, we cultured 418 strains of 10 microbial species on agar media. Using image processing techniques, we automatically extracted 10,048 colony images from 418 plate images. The dataset was created to perform deep learning using three models of the ResNet with 18, 50, and 101 layers. The deep learning models were evaluated using validation datasets comprising 75 strains. The accuracy of microbial classification reached 92.0% (69/75 strains) with the ResNet-50 (50-layer) model. This result indicates that this model has potential to support the microorganism identification in clinical laboratories.