计算机科学与探索2025，Vol.19Issue(10)：2615-2634,20.DOI:10.3778/j.issn.1673-9418.2502059
深度学习在黑色素瘤分类诊断中的研究进展
Research Progress of Deep Learning in Classification and Diagnosis of Melanoma
摘要
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 PH²and 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.关键词
黑色素瘤/深度学习/卷积神经网络/Transformer/迁移学习Key words
melanoma/deep learning/convolutional neural networks/Transformer/transfer learning分类
信息技术与安全科学引用本文复制引用
蒋润泽,刘静,马金刚,郭振,李明..深度学习在黑色素瘤分类诊断中的研究进展[J].计算机科学与探索,2025,19(10):2615-2634,20.基金项目
国家自然科学基金面上项目(82174528) （82174528）
山东省研究生教育优质课程和教学资源库建设项目(SDYKC20047,SDYAL2022041) （SDYKC20047,SDYAL2022041）
教育部产学合作协同育人项目(220606121142949).This work was supported by the National Natural Science Foundation of China General Program(82174528),the Graduate Education Quality Course and Teaching Resource Library Construction Project of Shandong Province(SDYKC20047,SDYAL2022041),and the Industry-University Cooperation and Collaborative Education Project of Ministry of Education of China(220606121142949). （220606121142949）
