Artifact-aware fungal detection in dermatophytosis: a transformer-based approach for KOH microscopy
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Dermatophytosis is commonly assessed using potassium hydroxide (KOH) microscopy, yet accurate recognition of fungal hyphae is hindered by preparation-related artifacts, heterogeneous keratin clearance, and notable inter-observer variability. This study presents a transformer-based object detection framework using the RT-DETR architecture for precise, query-driven localisation of fungal structures in high-resolution KOH images. A dataset of 2540 routinely acquired microscopy images was manually annotated using a multi-class strategy that explicitly distinguishes fungal elements from confounding artifacts, enabling the model to actively suppress false detections arising from visually similar mimics. To assess architectural trade-offs, RT-DETR was benchmarked against two CNN-based detectors (YOLOv11 and Faster R-CNN) under identical training and inference conditions. Five-fold stratified cross-validation was performed, and each fold-level model was evaluated on the same independent held-out test set (n = 254). Across the five evaluations, RT-DETR achieved a mean AP@0.50 of 89.73%±1.48%, a mean recall of 0.831±0.011, and a mean precision of 0.921±0.014. At the image level, the model achieved a mean sensitivity of 0.989±0.022 on the independent test set, with a mean of 0.2±0.4 missed positive cases across the five evaluations. These results demonstrate the technical feasibility of a transformer-based artificial intelligence (AI) system as a decision-support aid for fungal region detection in KOH microscopy, pending prospective multi-center validation to establish clinical generalisability.
Date Issued
2026-05-21
Date Acceptance
2026-05-15
Citation
Bioengineering, 2026, 13 (5)
ISSN
2306-5354
Publisher
MDPI AG
Journal / Book Title
Bioengineering
Volume
13
Issue
5
Copyright Statement
© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
License URL
Identifier
10.3390/bioengineering13050591
Subjects
deep learning
dermatophytosis
koh microscopy
object detection
Publication Status
Published
Article Number
ARTN 591