[Submitted on 1 Oct 2026]
Title:An AI-Based Multi-Stage Approach for Androgenetic Alopecia Assessment from Low-Magnification Scalp Images
View a PDF of the paper titled An AI-Based Multi-Stage Approach for Androgenetic Alopecia Assessment from Low-Magnification Scalp Images, by Mahmoud Raslan and 9 other authors
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Abstract:Androgenetic alopecia (AGA) is characterized by patterned follicular miniaturization, increased single-hair follicular units, and altered hair-shaft diameter. We present an automated quantitative scalp-analysis and clinical decision-support framework combining FU localization, ordinal visible-shaft counting, calibrated shaft-width estimation, regional aggregation, and an interpretable rule layer. The clinical cohort comprised 243 patients (127 AGA, 116 non-AGA), while the computer-vision experiments used 160 expert-annotated patients, 2,400 trichoscopic images, and approximately 158,000 FU annotations. Under patientdisjoint evaluation, YOLOv8m achieved test [email protected]=0.920 and recall=0.860; EfficientNet-B5 with a support-map channel achieved 87.0% expert-box count accuracy (macro F1=0.85). A separate 500-image set was processed end-to-end with detector-generated boxes, yielding MAE of 6.56 for follicle detection and 16.59 for follicle classification relative to human-expert annotations. The system is intended to assist, rather than replace, dermatologist interpretation.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.02421 [cs.CV]
(or arXiv:2610.02421v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.02421
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mohamed Hazem [view email] [v1] Thu, 1 Oct 2026 19:44:47 UTC (2,196 KB)
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