  The morbidity of scalp diseases is minuscule compared to other diseases, but
the impact on the patient's life is enormous. It is common for people to
experience scalp problems that include Dandruff, Psoriasis, Tinea-Capitis,
Alopecia and Atopic-Dermatitis. In accordance with WHO research, approximately
70% of adults have problems with their scalp. It has been demonstrated in
descriptive research that hair quality is impaired by impaired scalp, but these
impacts are reversible with early diagnosis and treatment. Deep Learning
advances have demonstrated the effectiveness of CNN paired with FCN in
diagnosing scalp and skin disorders. In one proposed Deep-Learning-based scalp
inspection and diagnosis system, an imaging microscope and a trained model are
combined with an app that classifies scalp disorders accurately with an average
precision of 97.41%- 99.09%. Another research dealt with classifying the
Psoriasis using the CNN with an accuracy of 82.9%. As part of another study, an
ML based algorithm was also employed. It accurately classified the healthy
scalp and alopecia areata with 91.4% and 88.9% accuracy with SVM and KNN
algorithms. Using deep learning models to diagnose scalp related diseases has
improved due to advancements i computation capabilities and computer vision,
but there remains a wide horizon for further improvements.

Computer Vision and Pattern Recognition;Artificial Intelligence;Computers and Society;Machine Learning