Semi-supervised mold differentiation using typical laboratory results as label data

Abstract

This study applies semi-supervised learning to automate the differentiation of mold colonies, thereby reducing the time and cost associated with air quality assessments. EfficientNet V2 and Normalization-Free Net (NfNet) were trained on a dataset of mold colony images, created in a semi-supervised way. NfNet demonstrated superior performance, particularly on non-padded images, with explainable AI techniques enhancing interpretability. The models exhibited generalization capabilities to environmental samples, indicating the potential for automating mold identification and streamlining air quality monitoring, thereby reducing manual effort and costs. Future work will focus on refining species handling and integrating the system into laboratory workflows.