Multi-Class Support Vector Machine Classification for Detecting Alopecia Areata and Scalp Diseases

Abstract

Alopecia Areata is a health condition marked by the absence of hair in specific regions, such as the scalp, face, and parts of the body. It occurs due to an autoimmune reaction where the body's immune system erroneously targets the hair follicles, leading to irregular hair loss patterns. It can affect people of all ages, genders, and ethnicities, and it is estimated to affect about 2% of the population worldwide. Timely identification and precise diagnosis of this condition are crucial in order to implement effective treatment strategies. The most common type of alopecia is alopecia areata (AA), which is typically detected and diagnosed using medical image processing models. In this study, we describe a unique method for image processing that incorporates a multiclass support vector machine classification approach. Our proposed methodology aims to attain accurate detection and categorization of a wide range of scalp issues, encompassing Alopecia Areata and other related conditions. The proposed approach entails capturing images of individuals with alopecia disease, enhancing the quality of the images through preprocessing techniques, and extracting distinctive features from scalp images using a range of image processing methods. Next, the extracted features are fed into a Multi-class SVM classifier, and a machine learning model is trained to achieve precise classification of the various conditions associated with alopecia areata. The evaluation of the proposed method using hair and scalp image databases demonstrates that the Multi-class SVM model achieves an accuracy of 89.3%, outperforming other models in terms of classification accuracy.