Artificial Intelligence & Deep Learning in Early Detection of Skin Cancer

Abstract

Skin cancer remnants one of the most dominant malignancies worldwide, where early detection is vital for improving patient survival and reducing treatment costs. Recent advances in artificial intelligence (AI) and deep learning (DL) have significantly transformed dermatologic diagnostics by enabling automated, accurate, and rapid analysis of skin lesion images. This review highlights the role of AI-driven technologies in early skin cancer detection, emphasizing the evolution from traditional rule-based systems to modern machine learning (ML) and deep learning approaches. Convolutional neural networks (CNNs), a key DL architecture, have demonstrated performance comparable to dermatologists in classifying benign and malignant lesions. The article discusses the complete workflow of AI-based dermatologic image analysis, including image acquisition, preprocessing, lesion segmentation, dataset annotation, model training, validation, and deployment. Performance evaluation metrics such as sensitivity, specificity, accuracy, and the area under the ROC curve are also considered critical for clinical reliability. Also, the integration of AI into tele dermatology platforms and mobile health requests has expanded access to dermatologic screening, chiefly in resource-limited settings. Despite promising outcomes, challenges such as dataset bias, need for high-quality annotations, interpretability, and regulatory concerns remain.Overall, AI and DL technologies hold substantial potential to enhance early skin cancer detection, reduce diagnostic variability, and support personalized and accessible dermatologic care.