Skin cancer is one of the most prevalent and deadly forms of cancer worldwide, which highlights the critical importance of early detection and diagnosis in improving patient outcomes. Deep learning (DL) has shown significant promise in enhancing the accuracy and efficiency of automated skin disease diagnosis, particularly in detecting and evaluating skin lesions and classification. However, there are still several challenges for DL-based skin cancer diagnosis, including complex features, image noise, intra-class variation, inter-class similarity, and data imbalance. By synthesizing recent research, this review discusses innovative approaches to cope with these challenges, such as data augmentation, hybrid models, and feature fusion, etc. Furthermore, the review highlights the integration of DL models into clinical workflows, offering insights into the potential of deep learning to revolutionize skin disease diagnosis and improve clinical decision-making. This article follows a comprehensive methodology based on the PRISMA framework and emphasizes the need for continued advancements to fully unlock the transformative potential of DL in dermatological care.
Liu et al. (Sat,) studied this question.
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