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    Automated Trichoscopic Analysis for Hair Disorder Classification Using Semantic Segmentation

    April 2026
    Naveenkumar N, U. Snekhalatha, Murali Narasimhan
    Studysummary This study developed an automated image analysis framework for diagnosing hair disorders using trichoscopic images, reporting a Random Forest classifier as having an 86.67% accuracy in distinguishing between different scalp pathologies based on quantitative image features.
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    Research cited in this study 3

    1. Enhanced Stratification of Male Pattern Hair Loss Using AI Through Novel Loss Region Ratio Analysis Scientific Reports · 2025
    2. Hair Cluster Detection Model Based on Dermoscopic Images Frontiers in physics · 2024
    3. Hair Follicle Classification and Hair Loss Severity Estimation Using Mask R-CNN Journal of Imaging · 2022