Diagnosis of Scalp Disorders using Machine Learning and Deep Learning Approach -- A Review

TL;DR

This review discusses recent advances in machine learning and deep learning techniques for diagnosing scalp disorders, highlighting high accuracy systems and potential for future improvements.

Contribution

It summarizes recent deep learning and machine learning approaches for scalp disorder diagnosis, emphasizing their accuracy and technological advancements.

Findings

Deep learning models achieve up to 99.09% accuracy in scalp disorder classification.

CNN-based systems effectively diagnose psoriasis with 82.9% accuracy.

ML algorithms like SVM and KNN classify healthy vs. alopecia with over 88% accuracy.

Abstract

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.