Optimized Machine Learning Models for Accurate Detection of Candida spp. in Gram-Stained Microscopy Images

Daniella Peña-Pedraza, Manuel Linares-Rufo, Franciso-Javier Bueno-Guillén, Carlos García-Bertolín, Harold Bermúdez-Marval, Alberto Garcéz-Jiménez, Alberto Garcéz-Jiménez, José-Manuel Gómez-Pulido, José-Manuel Gómez-Pulido

2025

Abstract

Image interpretation is crucial for clinical microbiological diagnosis. Manual reading of Gram-stained slides is timeconsuming and complex. The use of artificial vision systems based on machine learning (ML) models can speed up the detection of microorganisms of interest, ensuring that irrelevant images are discarded and those relevant for the diagnosis are considered. This automated pre-diagnosis process significantly reduces the burden on microbiologists and their subjectivity. It is possible to automate the morphological study of Gram-stained samples, through the identification of yeast-like cells or filamentous structures indicative of Candida spp. Several multiclass Machine Learning models (XGBoost, Artificial Neural Networks, and K-Nearest Neighbors) have been implemented, taking the relevant morphological characteristics from the images. The dataset dimensionality is optimized with innovative metaheuristic algorithms using objective functions for the specific detection of yeast and hypha. The best-optimized model achieved an accuracy of 0.821, precision macro of 0.827, recall macro of 0.790, and F1 macro of 0.806.

Paper Citation

in Harvard Style

Peña-Pedraza D., Linares-Rufo M., Bueno-Guillén F., García-Bertolín C., Bermúdez-Marval H., Garcéz-Jiménez A. and Gómez-Pulido J. (2025). Optimized Machine Learning Models for Accurate Detection of Candida spp. in Gram-Stained Microscopy Images. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS; ISBN 978-989-758-731-3, SciTePress, pages 571-578. DOI: 10.5220/0013170600003911

in Bibtex Style

@conference{bioinformatics25,
author={Daniella Peña-Pedraza and Manuel Linares-Rufo and Franciso-Javier Bueno-Guillén and Carlos García-Bertolín and Harold Bermúdez-Marval and Alberto Garcéz-Jiménez and José-Manuel Gómez-Pulido},
title={Optimized Machine Learning Models for Accurate Detection of Candida spp. in Gram-Stained Microscopy Images},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2025},
pages={571-578},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013170600003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS
TI - Optimized Machine Learning Models for Accurate Detection of Candida spp. in Gram-Stained Microscopy Images
SN - 978-989-758-731-3
AU - Peña-Pedraza D.
AU - Linares-Rufo M.
AU - Bueno-Guillén F.
AU - García-Bertolín C.
AU - Bermúdez-Marval H.
AU - Garcéz-Jiménez A.
AU - Gómez-Pulido J.
PY - 2025
SP - 571
EP - 578
DO - 10.5220/0013170600003911
PB - SciTePress