Computer Imaging of Alopecia Areata and Scalp Detection: A Survey

Computer Imaging of Alopecia Areata and Scalp Detection: A Survey

© 2022 by IJETT Journal
Volume-70 Issue-8
Year of Publication : 2022
Authors : C. Saraswathi, B. Pushpa
DOI : 10.14445/22315381/IJETT-V70I8P236

How to Cite?

C. Saraswathi, B. Pushpa, "Computer Imaging of Alopecia Areata and Scalp Detection: A Survey," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 347-358, 2022. Crossref,

Alopecia Areata (AA) is a frequent inflammatory affliction that causes erratic Hair Loss (HL). As with other resistant-influenced disorders, the development of AA is assumed to be the result of a complicated balance between surroundings and heredity. Various factors can cause hair loss, and trichoscopies and biopsies are usually required to ensure the cause of AA. There is currently no remedy for AA, although doctors can recommend various medications to support hair regrowth rapidly. AA does not immediately cause illness and is not communicable, but it is tough to adjust psychologically. Further, many people's experience with AA is regarded as a terrible infection that needs counselling for the mental and physical components of HL. So, an efficient HL detection system should be developed to tackle this emotional perceptive. Detecting the AA detection with scalp condition is required to find out the cause of AA level and can provide guidance for proper treatment. Computer vision using deep learning techniques is gaining significant attention because of improved performance over previous approaches. This article presents detailed analyses of different AA detection approaches using (Artificial Intelligence) AI techniques with modern Deep learning. First, AI-based frameworks designed by researchers in the past for different AA are studied briefly. After that, a comparative study is conducted to understand those frameworks' drawbacks and suggest new solutions to improve the AA detection system.

Artificial Intelligence, Alopecia Areata, Hair Loss (HL), Scalp Condition, Detection System.

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