Enriched Vitiligo Detection using ML and DL Approaches

The loss of melanocytes caused by the chronic autoimmune disease vitiligo leads in depigmented patches of skin that are frequently hard to distinguish from other hypopigmented disorders. Effective therapy depends on early and precise detection, but the diagnostic techniques used today are very difficult. Modern machine learning (ML) and deep learning (DL) techniques created for vitiligo detection and classification are reviewed and analyzed in this comprehensive work. Some the difficulties faced by deep learning models are overfitting, generalization, and poor data quality. Trust and interpretability are essential for therapeutic acceptance. Real-time processing and high computing requirements raise ethical and privacy issues. With a particular emphasis on distinguishing vitiligo from disorders that resemble scars, we investigate alternative convolutional neural networks (CNNs) and transfer learning methods that have been employed in previous research.