OD-Abs: A One-Class Outlier Detection-Based Approach for Classifying Images of Bacteria and Fungi from Abscesses

Czibula, Gabriela; Toader, Teodora-Alexandra; Mircea, Cristina

doi:10.15388/26-INFOR640

Informatica

OD-Abs: A One-Class Outlier Detection-Based Approach for Classifying Images of Bacteria and Fungi from Abscesses

Gabriela Czibula

Teodora-Alexandra Toader

Cristina Mircea

https://doi.org/10.15388/26-INFOR640

Pub. online: 7 August 2026 Type: Research Article

Open Access

Received
1 January 2026

Accepted
1 July 2026

Published
7 August 2026

Abstract

Accurately differentiating between fungal and bacterial infections is critical for treatment but remains challenging due to slow manual diagnostics and class imbalance in medical datasets. This paper introduces OD-Abs, a one-class outlier detection classifier based on an autoencoder architecture to distinguish infection types in microscopic images. By training solely on the reference fungal class, the model learns to identify bacterial instances as outliers. Our approach yields statistically significant improvements compared to other one-class approaches, achieving an F1-score of 0.967, an AUC of 0.980, and a specificity of 0.992. Additionally, we explore the explainability of our method by analysing reconstruction error maps from both a technical and biological perspective and conclude that the model’s decision-making aligns with biologically relevant morphological features. This proof of concept study establishes the OD-Abs autoencoder model as a highly robust tool for handling the task of classifying images of bacteria and fungi from abscesses.

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Biographies

Czibula Gabriela

https://orcid.org/0000-0001-7852-681X

gabriela.czibula@ubbcluj.ro

G. Czibula is a professor at the Computer Science Department, Faculty of Mathematics and Computer Science of the Babeş-Bolyai University, Romania. She published more than 220 papers in prestigious journals and conference proceedings. Her research interests include machine learning, distributed artificial intelligence and multiagent systems, and bioinformatics.

Toader Teodora-Alexandra

https://orcid.org/0009-0008-6447-5001

teodora.toader@ubbcluj.ro

T.-A. Toader is currently a third-year PhD student at the Doctoral School in Mathematics and Computer Science of the Babeş-Bolyai University, in Cluj-Napoca, Romania. Her research interests include computer vision and deep learning.

Mircea Cristina

cristina.mircea@ubbcluj.ro

C. Mircea is an assistant professor at the Department of Molecular Biology and Biotechnology, Faculty of Biology and Geology, Babeş-Bolyai University, Romania. She has expertise in isolation, identification, and characterization of bacteria and fungi from a wide range of environments.

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one-class classification outlier detection encoder-decoder architectures bacterial versus fungal abscesses classification

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