TLDR A web platform was created to help diagnose scalp conditions accurately and easily.
The study developed a machine learning model using EfficientNet-B0 to diagnose scalp conditions like fine dandruff and perifollicular erythema, achieving accuracies of 75% and 82%, respectively. A web platform was created for users to upload images, receive diagnostic results, and view similar cases and solutions. The platform was highly rated for usability, design, and user experience, making it accessible and beneficial for early detection and treatment of scalp conditions, thus promoting global scalp health.
4 citations
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October 2022 in “Journal of Imaging” An intelligent system can classify hair follicles and measure hair loss severity with reasonable accuracy.
8 citations
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January 2022 in “Sensors” Deep learning can accurately automate hair density measurement, with YOLOv4 performing best.
January 2022 in “Journal of Pharmaceutical Negative Results” The VGG-SVM method accurately identifies and classifies stages of Alopecia Areata and other hair loss conditions.
December 2021 in “Acta dermato-venereologica” A deep learning model accurately predicts male hair loss types using scalp images.
12 citations
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November 2016 in “International Journal of Women's Dermatology” Various hair camouflage options help people with hair loss improve appearance.
29 citations
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July 2003 in “PubMed” Hair loss affects both genders and can impact well-being, with treatments available for various types.
2 citations
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June 2020 in “Skin Research and Technology” MRI reveals thinner scalps and smaller hair follicles in people with androgenetic alopecia.
