Deep Review on Alopecia Areata Diagnosis for Hair Loss-Related Autoimmune Disorder
July 2022
in “
International Journal of Applied Pharmaceutics
”
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Studysummary This research explored the use of machine learning and deep learning methods to accurately identify alopecia areata in humans by analyzing facial images and demonstrated the potential of these techniques for medical, security, and commercial applications.
Our plain-language summary. Not medical advice or a treatment recommendation. Consult a qualified healthcare professional before changing treatment. Full disclaimer
The document discusses the application of machine learning and deep learning strategies for diagnosing alopecia areata, a chronic hair loss condition. The authors note the rising global incidence of hair thinning in women and the genetic factors involved. They suggest using machine learning techniques, which have proven effective in various fields, including dermatology, for improved prediction and diagnosis of alopecia areata. The study uses deep learning algorithms to determine hair loss levels in men from facial images, using a specially created database of face photos with different baldness levels. The results show the potential and efficiency of these methods for medical, security, and business uses. The study's main goal is to assess the accuracy of these machine learning and deep learning strategies in identifying alopecia in humans.
