Artificial Intelligence in Dermoscopy: A Review of Advances and Future Directions

Abstract

Skin malignancy represents one of the mainly common and deadliest diseases, and early recognition is vital for successful treatment. In this context, the review provides a complete overview of the recent artificial intelligence developments designed for automated skin malignancy identification, focusing on the primary role of the deep learning architecture, particularly CNN , in terms of the diagnostic usability increase. The numerous approaches, such as ensemble models, multimodal fusion strategies, and hybrid mechanisms combining deep learning technologies with traditional machine learning using deep features. The open-source datasets, represented by ISIC and HAM10000 opportunities, substantially contributed to the rapid model development, but even now, classification challenges related to data distribution irregularity and image variance remain. Feature extraction with traditional preprocessing methods, like normalization, augmentation, and segmentation, significantly improved the classification performance.High-quality datasets integration, the implementation of advanced feature extraction with fusion strategies, lays down the basics for the intelligent, scalable skin cancer detection systems, with ample application potential in the real-world clinical practice.

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