Skin cancer, one of the most common malignancies worldwide, necessitates early detection for better patient outcomes and efficient treatment. Recent advancements in deep learning have shown significant promise in enhancing the accuracy and efficiency of skin cancer diagnosis. This review comprehensively examines the current state of deep learning-based approaches for skin cancer detection, highlighting key methodologies, datasets, and performance metrics. We explore the integration of Convolutional Neural Networks (CNNs), Transfer Learning, and Attention Mechanisms in dermatological imaging analysis. Additionally, we discuss the impact of modified attention mechanisms, such as spatial and channel attention, in improving model performance by focusing on critical features of skin lesions. The review also addresses challenges related to data quality, class imbalance, and model interpretability. By synthesizing findings from recent studies, this review aims to provide a detailed understanding of how deep learning technologies are transforming skin cancer detection and to identify future research directions that could further enhance diagnostic accuracy and clinical applicability.

Deep Learning, Machine Learning, Convolutional Neural Network, Transfer Learning, Attention Mechanism, Skin Cancer Detection

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