Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

    January 2022 in “ Sensors ”
    Minki Kim, Sunwon Kang, Byoung-Dai Lee
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    TLDR Deep learning can accurately automate hair density measurement, with YOLOv4 performing best.
    The study "Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks" used deep learning models to automate Hair Density Measurement (HDM), a method for assessing hair loss severity. The models, EfficientDet, YOLOv4, and DetectoRS, were trained on 4492 hair scalp images from 817 male patients. YOLOv4 outperformed the other models with a mean average precision of 58.67 and showed the highest precision (80.75%), recall (80.22%), and accuracy (75.73%). However, all models struggled to detect Class 3 hair follicles, likely due to class imbalance and feature similarity with Classes 1 and 2. The study concluded that deep learning can provide accurate automated HDM with sufficient training data, suggesting potential for improved efficiency and accuracy in hair loss assessment.
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