An Analysis of Alopecia Areata Classification Framework for Human Hair Loss Based on VGG-SVM Approach

January 2022 in “ Journal of Pharmaceutical Negative Results ”

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Alopecia Areata hair loss VGG-SVM machine learning classification diagnosing monitoring

Studysummary This study found that a VGG-SVM model using machine learning techniques achieved 98.31% accuracy in distinguishing alopecia areata from healthy hair based on image datasets.

Automatically generated from the study's abstract, not written by a person, and not a review of the full paper. Not medical advice or a treatment recommendation. Read the original study, and consult a qualified healthcare professional before changing treatment. Full disclaimer ·

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