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Updated: Mar 29, 2026

Use of Image Cytometry for Quantification of Pathogenic Fungi in Association with Host Cells

Use of Image Cytometry for Quantification of Pathogenic Fungi in Association with Host Cells

Published on: June 19, 2013

Machine Learning-Assisted Classification of Pathogenic Yeasts Using Laser Light Scattering and Conventional

Xiaoxuan Liu¹, Shamanth Shankarnarayan², Zexi Cheng¹

¹Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.

Journal of Imaging

|March 27, 2026

Summary

This summary is machine-generated.

Accurately identifying pathogenic yeasts like Candida auris is crucial for effective treatment. This study uses machine learning on laser light scattering and microscopy images, achieving high accuracy in classifying yeast species.

Keywords:

label-free identification light scattering machine learning microscopy image classification pathogenic yeasts

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Last Updated: Mar 29, 2026

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Published on: June 19, 2013

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4D Microscopy of Yeast

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Published on: April 28, 2019

Area of Science:

Medical Mycology
Computational Biology
Biophotonics

Background:

Antifungal drug resistance in pathogenic yeasts, particularly multidrug-resistant Candida auris, poses significant clinical challenges.
Accurate and rapid identification of yeast pathogens is essential for improving patient treatment outcomes and infection control.

Purpose of the Study:

To develop and evaluate a machine learning-based technique for identifying pathogenic yeast species.
To assess the efficacy of using laser light scattering patterns and conventional microscopy images for yeast classification.

Main Methods:

A neural network model (DenseNet-201) was employed for image analysis.
Binary classification was performed on seven pathogenic yeast species using data from laser light scattering and microscopy images.
Performance was evaluated by calculating classification accuracy for individual species and for isolating Candida auris.

Main Results:

High average classification accuracy was achieved: 95.3% for light scattering patterns and 96.6% for microscopy images.
Excellent accuracy was demonstrated in isolating Candida auris from other species, with averages of 95.1% (light scattering) and 96.7% (microscopy).

Conclusions:

Machine learning models, when combined with laser light scattering and microscopy image data, show significant potential for accurate pathogenic yeast classification.
This approach offers a promising method for identifying key yeast pathogens, aiding in clinical diagnostics and treatment strategies.