Artefact-Aware Fungal Detection in Dermatophytosis: A Real-Time Transformer-Based Approach for KOH Microscopy

Rana Gursoy; Abdurrahim Yilmaz; Baris Kizilyaprak; Esmahan Caglar; Burak Temelkuran; Huseyin Uvet; Ayse Esra Koku Aksu; Gulsum Gencoglan

cs.CVFeb 22, 2026

Artefact-Aware Fungal Detection in Dermatophytosis: A Real-Time Transformer-Based Approach for KOH Microscopy

Authors: Rana Gursoy, Abdurrahim Yilmaz, Baris Kizilyaprak, Esmahan Caglar, Burak Temelkuran, Huseyin Uvet, Ayse Esra Koku Aksu, Gulsum Gencoglan

Organizations: Department of Mechatronics Engineering, Yildiz Technical University · Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London · Department of Dermatology and Venereology, Istanbul Research and Training Hospital · Department of Bioengineering, Yildiz Technical University ·

Abstract

Dermatophytosis is commonly assessed using potassium hydroxide (KOH) microscopy, yet accurate recognition of fungal hyphae is hindered by artefacts, heterogeneous keratin clearance, and notable inter-observer variability. This study presents a transformer-based detection framework using the RT-DETR model architecture to achieve precise, query-driven localization of fungal structures in high-resolution KOH images. A dataset of 2,540 routinely acquired microscopy images was manually annotated using a multi-class strategy to explicitly distinguish fungal elements from confounding artefacts. The model was trained with morphology-preserving augmentations to maintain the structural integrity of thin hyphae. Evaluation on an independent test set demonstrated robust object-level performance, with a recall of 0.9737, precision of 0.8043, and an [email protected] of 93.56%. When aggregated for image-level diagnosis, the model achieved 100% sensitivity and 98.8% accuracy, correctly identifying all positive cases without missing a single diagnosis. Qualitative outputs confirmed the robust localization of low-contrast hyphae even in artefact-rich fields. These results highlight that an artificial intelligence (AI) system can serve as a highly reliable, automated screening tool, effectively bridging the gap between image-level analysis and clinical decision-making in dermatomycology.

Figures & tables

Figure 1: Schematic overview of the study workflow: clinical sample collection, KOH preparation, microscopic imaging, expert annotation, and artificial intelligence modelling steps.

Category	Component	Count / Value
Images	Total Microscopic Frames	2,540
Resolution	Pixel Dimensions	$2048\times 2048$
Annotations	Fungal Elements (Positive Class)	631
	Confounding Artefacts (Negative Class)	381

Table 1: Descriptive statistics of the experimental dataset used for training and evaluation, highlighting the multi-class annotation strategy.

Figure 2: (a,b) Representative KOH microscopy image illustrating the multi-class annotation strategy used in the dataset. Clear fungal hyphae are marked with green bounding boxes, while confounding artefactual structures (e.g., fibers, refractive edges) are highlighted with purple boxes. Insets provide magnified views of the annotated regions of interest.

Figure 3: Overview of the model fungal detection pipeline. KOH microscopy images are preprocessed via letterbox resizing and normalization, then passed through the model architecture, which combines a CNN backbone, hybrid encoder, attention-based query selection, and a transformer decoder to produce bounding box predictions for both fungal elements and confounding artefacts. The model is trained using AdamW with cosine learning-rate scheduling, and inference is performed in real time with a confidence threshold of 0.25.

Evaluation Level	Metric	Value
Object-Level	Precision	0.8043
(Detection Performance)	Recall (Sensitivity)	0.9737
	F1-Score	0.8810
	[email protected] (%)	93.56
	[email protected]:0.95 (%)	78.40
	Mean IoU	0.8560

Table 2: Comprehensive performance evaluation of the model, encompassing both object-level detection and image-level diagnostic classification.

Figure 4: Image-level confusion matrix evaluating diagnostic performance across 254 test samples (89 positive, 165 negative). The model correctly identified all infected images with zero false negatives, yielding 98.8% overall accuracy.

Figure 5: Comprehensive qualitative evaluation and failure analysis of the proposed model. (a–c) Expert-defined Ground Truth (GT) annotations illustrating the primary training categories: fungal elements (fungi; green bounding boxes) and artefacts (purple bounding boxes). (d–f) Model inference results showing predicted fungal elements with corresponding confidence scores; panel (e) highlights successful discrimination between true fungal structures and visually similar artefactual mimics. (g–i) Comparative overlays of GT annotations and model predictions, including fungal detections (blue bounding boxes) and artefact predictions (orange bounding boxes), enabling direct visual assessment of class-wise localization performance. The high spatial overlap and correct class assignment across heterogeneous backgrounds demonstrate the model’s robustness against common KOH microscopy artefacts.

Microscopy Image Analysis

Microscopy image analysis uses computational methods to detect, segment, track, reconstruct, and quantify structures in scientific images.

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Mycosis fungoides (MF) is a rare form of cutaneous T-cell lymphoma that is often misdiagnosed in early stages due to its visual similarity to benign inflammatory dermatoses. Early and accurate diagnosis is critical for improving patient outcomes. In this paper, we propose a comprehensive diagnostic framework for automated MF detection that combines dual- scale histopathological image analysis with deep learning. To distinguish MF from other lymphoproliferative skin conditions, the proposed approach leverages a late-fusion ensemble of dual- magnification (10x and 20x) convolutional neural networks (CNNs), complemented by a random forest classifier trained on 16 clinical features. Experimental results on an expanded dataset of 6,267 images (4,306 MF; 1,961 Non-MF) across 463 patients demonstrate that strong detection performance is obtained by prioritizing higher-resolution cytological details (20x) within broader architectural context (10x). The image-based late-fusion model achieves an accuracy of 83.58% and a sensitivity of 89.13%, while the clinical random forest model achieves an accuracy of 96.6% and sensitivity of 93.8%, highlighting the po- tential of this multimodal framework as a robust clinical decision support system in dermatology. This framework addresses two distinct clinical objectives: an image-based dual-scale pipeline optimized for the early diagnostic screening of MF versus non- MF dermatoses, and a complementary clinical metadata model designed for the subsequent staging of confirmed MF cases (patch/plaque versus tumor)

Abstract

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