Department
School of Data Science and Analytics
Additional Department
Molecular and Cellular Biology
Document Type
Article
Publication Date
5-21-2025
Embargo Period
10-27-2025
Abstract
Pathogenic yeasts are an increasing concern in healthcare, with species like Candida auris often displaying drug resistance and causing high mortality in immunocompromised patients. The need for rapid and accessible diagnostic methods for accurate yeast identification is critical, especially in resource-limited settings. This study presents a convolutional neural network (CNN)-based approach for classifying pathogenic yeast species from microscopy images. Using transfer learning, we trained the model to identify six yeast species from simple micrographs, achieving high classification accuracy (93.91% at the patch level, 99.09% at the whole image level) and low misclassification rates across species, with the best performing model. Our pipeline offers a streamlined, cost-effective diagnostic tool for yeast identification, enabling faster response times in clinical environments and reducing reliance on costly and complex molecular methods.
Journal Title
Pathogens
Journal ISSN
2076-0817
Volume
14
Issue
5
First Page
504
Digital Object Identifier (DOI)
10.3390/pathogens14050504
Comments
This article received funding through Kennesaw State University's Faculty Open Access Publishing Fund, supported by the KSU Library System and KSU Office of Research.