Skin Disease Classification Using Machine Learning Algorithms: A Review

Skin diseases pose significant health challenges worldwide, affecting millions of individuals and leading to diverse clinical manifestations. In recent years, machine learning algorithms have emerged as powerful tools for automated skin disease classification, aiding dermatologists in accurate and timely diagnosis. This review paper presents a comprehensive overview of the state-of-the-art in skin disease classification using machine learning techniques. The paper begins by providing an overview of common skin diseases and the challenges faced in their diagnosis. It then delves into the fundamental concepts of machine learning, including supervised, unsupervised, and deep learning algorithms. Next, the review paper analyzes various publicly available skin disease datasets used for training and testing machine learning models. Evaluation metrics commonly employed to assess the performance of the algorithms are discussed, emphasizing accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. Furthermore, the review outlines the recent advancements in transfer learning and data augmentation techniques in the context of skin disease classification, which have shown promise in improving model generalization and performance. The paper concludes with a critical analysis of the challenges and limitations faced in this domain, including class imbalance, data privacy, and interpretability of machine learning models. Potential future research directions and emerging trends in skin disease classification using machine learning algorithms are also highlighted. Overall, this review paper serves as a valuable resource for researchers, dermatologists, and medical practitioners interested in understanding the current state of skin disease classification using machine learning algorithms.