A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet

    August 2024 in “ Applied Sciences ”
    Yea-Ju Jin, Yeon-Soo Park, Seong-Ho Kang … Ji-Yeoun Lee
    Studysummary In this study, researchers developed a machine learning model that accurately diagnosed scalp conditions like fine dandruff and perifollicular erythema with 75% and 82% accuracy, respectively, and created a user-friendly web platform for scalp health self-assessment, which achieved high user satisfaction.
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    Research cited in this study 8

    1. Development of a New Classification and Scoring System for Scalp Conditions: Scalp Photographic Index Journal of Dermatological Treatment · 2023
    2. Hair Follicle Classification and Hair Loss Severity Estimation Using Mask R-CNN Journal of Imaging · 2022
    3. Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks Sensors · 2022
    4. An Analysis of Alopecia Areata Classification Framework for Human Hair Loss Based on VGG-SVM Approach Journal of Pharmaceutical Negative Results · 2022
    5. Deep Learning-Based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia Acta dermato-venereologica · 2021
    6. ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health IEEE Access · 2020
    7. Hair Camouflage: A Comprehensive Review International Journal of Women's Dermatology · 2016
    8. Common Hair Loss Disorders PubMed · 2003

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