关键词: 黑色素瘤, 深度学习, 卷积神经网络, Transformer, 迁移学习

Abstract: As the most lethal type of skin cancer, early and accurate diagnosis of melanoma is essential to improve the survival rate of patients. In recent years, deep learning technology has shown great potential in the field of melanoma classification and diagnosis, providing new technical support for clinical diagnosis. This paper systematically reviews the research progress of deep learning in melanoma classification, focusing on the technical evolution and clinical application of core methods such as convolutional neural networks, Transformers, generative adversarial networks and recurrent neural networks. Firstly, the characteristics of authoritative datasets such as HAM10000, ISIC, and PH2 and their value in algorithm development are summarized, and the preprocessing methods and enhancement strategies of different datasets are analyzed in detail, which provides a high-quality data basis for model training. Secondly, the improvement strategies of different deep learning models are deeply analyzed, including network architecture optimization, multimodal feature fusion, and data imbalance processing. In addition, the role of multiple learning strategies such as transfer learning and ensemble learning in improving model performance is also discussed. Finally, the limitations of current technology are summarized, and future research directions are prospected, including the application prospects of multimodal large models, federated learning and lightweight technology.

Key words: melanoma, deep learning, convolutional neural networks, Transformer；transfer learning