Machine Learning Algorithm for Classification of Alopecia Areata from Human Scalp Hair Images

Abstract

Alopecia Areata is defined as an autoimmune disorder that causes hair loss. Millions of people around the world are affected by this disorder. Machine learning (ML) techniques have demonstrated potential in different fields of dermatology. We proposed the classification of Alopecia Areata (AA) types from the human scalp hair images. First, scalp hair images from both the healthy and different AA types are acquired and pre-processed to improve the global contrast of those images. Then, color, texture, and shape characteristics are extracted from the pre-processed images. Then, an artificial algae algorithm (AAA) is applied to choose the most relevant characteristics. Further, modified extreme learning machine (MELM) and wavelet neural network (WNN) are proposed to learn the chosen characteristics, which creates trained ML models. Such trained models are used to classify the new images into various classes of AA. At last, the experimental outcomes reveal that the AAA-WNN and AAA-MELM on scalp hair image corpus achieve 90.37 and 92.64% accuracy compared to the classical ML algorithms for AA classification and diagnosis.