DEEP REVIEW ON ALOPECIA AREATA DIAGNOSIS FOR HAIR LOSS-RELATED AUTOIMMUNE DISORDER PROBLEM
DOI:
https://doi.org/10.22159/ijap.2022.v14ti.19Keywords:
Alopecia areata, Hair loss, Deep learning, Machine learning, Feature extraction approachesAbstract
Lots of women all over the globe are affected by thinning hair, and the number of females suffering from the disease is growing per year. Another important component in the development of thinning hair is genetics. One of the most important goals is to make a clinical condition. For example, in the area of medicine, categorization is critical since one of the primary goals of the doctor is to determine whether or not a patient suffers from an illness. Alopecia areata is a kind of chronic illness that causes baldness in the affected region. AA may cause baldness for a variety of causes thus, testing may be essential to confirm if it is the source of the loss of hair. Machine learning approaches have shown promise in a variety of fields, including dermatology, and may be useful in identifying alopecia areata for better prediction and diagnosis. Proper detection of an illness is also influenced by the fluctuating character of illness signs. Deep learning algorithms for identifying hair loss levels in males using facial pictures in this research. In this situation, a special training database, including face photos with varying degrees of baldness, has been generated. Furthermore, despite the limited accessibility of hairs in such images, a matching approach for mechanically categorizing face images to design categorization tables of male baldness from the medical field is provided. The outcomes of the experiments demonstrate the potential and efficiency for medical, security, and business apps. Related work in machine learning for hair illness categorization has also been addressed. The main objective of this study to analyze several machine learning and deep learning strategies for the identification of alopecia as well as in humans, as well as to determine the accuracy of extracting features methodologies.
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