使用机器学习和深度学习诊断皮肤疾病的最新进展和前景:一篇评论
Junpeng Zhang1, Fan Zhong1, Kaiqiao He2
1College of Electrical Engineering, Sichuan University, Chengdu 610017, China.
Diagnostics (Basel, Switzerland)
|December 9, 2023
概括
与传统的机器学习相比,深度学习显著提高了皮肤疾病诊断的准确性. 未来的研究应该解决数据集多样性,模型概括性,可解释性,并将重点扩展到黑色素瘤之外.
科学领域:
- 皮肤病学 皮肤病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 皮肤疾病是一个主要的健康问题.
- 机器学习 (ML) 和深度学习 (DL) 提高了诊断准确性和治疗.
- 本综述侧重于皮肤疾病诊断中的ML和DL.
研究的目的:
- 在皮肤疾病诊断中全面审查ML和DL.
- 专注于最近的深度学习方法.
- 分析挑战并提出解决方案.
主要方法:
- 在IEEE,Springer,Web of Science,PubMed (过去5年) 的文献搜索.
- 包括29篇关于皮肤学图像细分和45篇关于分类的文章.
- 分类研究传统的ML和DL算法进行比较分析.
主要成果:
- 深度学习方法在皮肤病诊断中比传统的ML更有效.
- 对于改进算法准确性的存在显著的空间.
- 挑战包括数据集多样性,模型概括性和可解释性.
结论:
- 深度学习对皮肤病诊断有很大的潜力.
- 未来的研究需要提高准确性,解决数据集的局限性,提高模型的可解释性.
- 研究应该扩大重点,包括黑色素瘤以外的更多有色素的皮肤疾病.
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