December 2021 in “Acta dermato-venereologica” This study developed a deep learning framework and quantitative model that accurately predict basic and specific classification in male androgenetic alopecia by analyzing trichoscopic images.
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November 2024 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.
March 2023 in “Applied and Computational Engineering” This study proposes a deep learning model using CNN with VGG16, VGG19, and MobileNetV2 architectures, achieving high accuracy in classifying scalp diseases from images, potentially facilitating diagnosis and treatment via mobile devices.
April 2021 in “Journal of Investigative Dermatology” A deep learning model was developed to help diagnose trichothiodystrophy by analyzing hair patterns.
April 2026 in “International Journal of Engineering Research and Science & Technology” This study reports that an Explainable AI-based hair health prediction system using a novel hybrid model outperformed traditional machine learning methods, achieving high accuracy in predicting key factors and providing personalized recommendations.