Skin Diseases Classification with Machine Learning and Deep Learning Techniques: A Systematic Review

cancer is one of the most prevalent types of cancer worldwide, and its early detection is crucial for improving patient outcomes. Artificial Intelligence (AI) has shown significant promise in assisting dermatologists with accurate and efficient diagnosis through automated skin disease classification. This systematic review aims to provide a comprehensive overview of the various AI techniques employed for skin disease classification, focusing on their effectiveness across different datasets and methodologies. A total of 220 articles were initially identified from databases such as Scopus and IEEE Xplore. After removing duplicates and conducting a title and abstract screening, 213 studies were assessed for eligibility based on predefined criteria such as study relevance, clarity of results, and innovative AI approaches. Following full-text review, 56 studies were included in the final analysis. These studies were categorized based on the AI techniques used, including Convolutional Neural Networks (CNNs), Transformer-based models, hybrid models combining CNNs with other techniques, Generative Adversarial Networks (GANs), and ensemble learning approaches. The review highlights that the ISIC dataset and its variations are the most commonly used data sources, owing to their extensive and diverse collection of dermoscopic images. The results indicate that CNN- based models remain the most widely adopted and effective approach for skin disease classification, with several hybrid and Transformer-based models also demonstrating high accuracy and specificity. Despite the advancements, challenges such as dataset variability, the need for more diverse training data, and the lack of interpretability in AI models persist. This review provides insights into current trends and identifies future directions for research, emphasizing the importance of integrating AI into clinical practice for improved skin disease management.