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首页 > 过刊浏览>2024年第33卷第12期 >1-15. DOI:10.15888/j.cnki.csa.009708
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深度学习在皮肤癌病变分类诊断中的应用进展
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国家自然科学基金面上项目(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); 教育部产学合作协同育人项目(220606121142949)


Advances in Deep Learning for Classification and Diagnosis of Skin Cancer Lesion
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    摘要:

    皮肤癌是最常见和最致命的癌症类型之一, 患病数量在世界范围内急剧增加. 如果没有在早期阶段诊断出来, 它可能转移, 导致高死亡率. 结合近几年的相关文献就传统机器学习和深度学习两种学习方法在皮肤癌病变诊断中的应用作一系统综述, 为皮肤癌诊断的深入研究提供相应的参考价值, 首先对几个皮肤病公共可获取数据集进行整理, 其次分析和比较不同的学习算法在皮肤癌病变分类中的应用, 更好地了解它们在实际应用中的优势和局限性, 重点阐述在卷积神经网络领域的分类诊断. 在深入了解这些算法的基础上, 还将探讨它们在处理皮肤疾病时的性能差异、改进思路. 最终, 通过对当前挑战和未来发展方向的探讨, 将为进一步提升皮肤癌早期诊断系统的性能和可靠性提供有益的参考和建议.

    Abstract:

    Skin cancer is one of the most common and deadliest types of cancer, with its incidence rapidly increasing worldwide. Failure to diagnose it in its early stages can lead to metastasis and high mortality rates. This study provides a systematic review of recent literature on the application of traditional machine learning and deep learning in the diagnosis of skin cancer lesions, providing valuable reference for further research in skin cancer diagnosis. Firstly, several publicly available datasets of skin diseases are compiled. Secondly, the application of different machine learning algorithms in the classification of skin cancer lesions is analyzed and compared to better understand their advantages and limitations in practical applications, with a focus on convolutional neural network in diagnosis classification. With a thorough understanding of these algorithms, their performance differences and improvement strategies in dealing with skin diseases are discussed. Ultimately, through discussions on current challenges and future directions, beneficial insights and recommendations are provided to further enhance the performance and reliability of early skin cancer diagnosis systems.

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刘天宇,刘静,马金刚,陈天真,李明.深度学习在皮肤癌病变分类诊断中的应用进展.计算机系统应用,2024,33(12):1-15

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  • 收稿日期:2024-05-30
  • 最后修改日期:2024-06-26
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