2025-09

International Conference on Advanced Science and Engineering (ICOASE2025)

Extensive development in deep learning has contributed to a revolution in ailment detection in the medical sector, specifically in dermatology. Proper and timely diagnosis of skin diseases is extremely crucial to treat them effectively, and for better quality of life among patients. This paper presents a comprehensive description of modern deep learning methods which are utilized for automation in diagnosis systems through Convolutional Neural Networks (CNNs) and other transfer learning methods for dermatological condition diagnosis. For effective training of a classifier large data sets (e.g., HAM10000 and ISIC) are essential. Nevertheless, data imbalance and heterogeneity in lesions, overfitting are problems. But using ensemble learning, attention mechanism, explainable AI, data augmentation, hybrid model, task-specific loss function, we can significantly improve classification accuracy, model interpretability, and robustness. Through integration of these developments, this review highlights deep learning's transformational capability in dermatology toward promoting large-scale, accurate, yet affordable diagnostic aids for clinical expertise. The work finally also identifies ethical and practical concerns associated with adopting AI systems in real-world health care.