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

TL;DR

This paper introduces a machine learning method using laser light scattering and microscopy images to accurately identify pathogenic yeasts, including drug-resistant Candidozyma auris.

Contribution

A novel machine learning approach combining laser light scattering and microscopy for high-accuracy yeast classification is proposed.

Findings

Binary classification of seven yeast species achieved 95.3% accuracy using light scattering patterns.

Microscopy images achieved 96.6% classification accuracy for yeast species.

Candidozyma auris was isolated with 95.1% accuracy using light scattering and 96.7% using microscopy.

Abstract

Yeast infections are a major concern in clinical settings, and several known species are recognized for their antifungal drug resistance, especially the multidrug-resistant pathogen Candidozyma auris. It is of increasing importance to identify pathogenic yeasts to improve treatment outcomes. We present a technique to identify these yeast pathogens using machine learning with a neural network (DenseNet-201) on images obtained from laser light scattering and conventional microscopy. We performed the binary classification of seven species of pathogenic yeast based on their light scattering patterns and their microscopy images. We achieved an average classification accuracy of 95.3% for light scattering patterns and 96.6% for microscopy images of the yeast cells. We also demonstrate high classification accuracy when isolating Candidozyma auris images from all other species combined, at an average of 95.1% for light scattering patterns and 96.7% for microscopy images. The high average classification accuracies suggest that both light scattering and microscopy image data can be combined with machine learning models to classify pathogenic yeasts.

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