Detection and Classification of Alopecia Areata Using Diverse Feature-Set

Abstract

Alopecia areata is a prevalent autoimmune disorder resulting in hair loss on the scalp and other body parts, affecting millions worldwide. This condition can significantly impact one's psychological well-being and self-esteem, highlighting the importance of early detection for better disease management and potential hair regrowth. This study uses computer vision techniques to propose a comprehensive method for detecting alopecia areata from camera images. Two distinct datasets, Dermnet for alopecia areata images and Figaro1k for healthy images are employed, with a preprocessing phase involving histogram equalization to enhance image quality. Subsequently, color, texture, and shape features are extracted from the images, followed by feature fusion and selection to maximize the discriminative power of the dataset. Several popular classifiers, such as Random Forest, SVM, Decision Tree, Logistic Regression, Naive Bayes, ANN, and KNN, are employed to identify the most effective models. The proposed technique achieves a remarkable accuracy of 96.43%, outperforming related research methods. This study shows considerable promise for improving the early detection and treatment of Alopecia Areata by utilizing sophisticated computer vision and machine learning methods, leading to enhanced patient outcomes and a better quality of life.