Deep Learning Approaches for Hair Disease Classification: A Comparative Analysis of MobileNetV2 and VGG19 Architectures

    November 2024
    Pratham Kaushik, Sunila Choudhary
    Studysummary In this study, VGG19 slightly outperformed MobileNetV2 in hair disease classification accuracy, achieving 98% compared to MobileNetV2's 97%. However, MobileNetV2 was faster and more computationally efficient, making it suitable for resource-limited settings.
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    Research cited in this study 5

    1. Hair and Scalp Disease Detection Using Machine Learning and Image Processing INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2024
    2. Methods of Transfer Learning for Multiclass Hair Disease Categorization 2023
    3. Determination of Cortisone and Cortisol in Human Scalp Hair Using an Improved LC-MS/MS-Based Method Clinical Chemistry and Laboratory Medicine (CCLM) · 2023
    4. Diagnosis of Hair Disorders During the COVID-19 Pandemic: An Introduction to Teletrichoscopy Journal of the European Academy of Dermatology and Venereology · 2020
    5. ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health IEEE Access · 2020