Diagnosis of Scalp Disorders using Machine Learning and Deep Learning Approach – A Review
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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 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 Psoriasis using the CNN with an accuracy of 82.9 ML based algorithm was also employed. It accurately classified the healthy scalp and alopecia areata with 91.4 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.
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