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一种混合模型,用于使用视觉变压器和稳定的扩散来改进指甲病的分类.

Rahul Nijhawan1, Ananya Gupta1, Manoj Diwakar2,3

  • 1Thapar Institute of Engineering and Technology, Patiala, Punjab, India.

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|January 12, 2026
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概括

通过稳定扩散模型生成的合成数据可以提高用于诊断指甲疾病的机器学习准确性. 这种方法增强了诊断工具,导致更准确和及时的治疗条件,如真菌感染和牛皮.

关键词:
数据增强数据增强爪病是指甲疾病的一种疾病.指甲病数据集指甲病数据集稳定的扩散 稳定的扩散文本到图像转换器转移学习转移学习

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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:

  • 爪病很常见,通常通过视觉检查来诊断,这可能导致不准确和治疗延迟.
  • 准确和及时的诊断对于有效管理指甲疾病,如真菌感染,paronychia和牛皮至关重要.

研究的目的:

  • 研究通过稳定扩散模型生成的合成指甲病数据的使用,以提高机器学习的诊断准确性.
  • 加强数据转换技术,使用文本到图像稳定扩散模型中的少数镜头学习来生成多样化的合成数据.

主要方法:

  • 通过使用稳定扩散模型与少数射击学习生成合成指甲病数据.
  • 合成数据的应用以增强定制的真实世界指甲疾病数据集.
  • 预训练的卷积神经网络 (CNN) MobileNetV2和视觉转换器模型与增强数据集的评估.

主要成果:

  • 合成数据提高了MobileNetV2和视觉变压器模型的稳定性.
  • 移动NetV2实现了3.26%的精度增加.
  • 视觉变压器实现了3.02%的精度增加.

结论:

  • 由稳定扩散模型生成的合成数据有效地提高了机器学习模型用于指甲疾病分类的性能.
  • 这种方法为提高自动指甲病诊断的准确性和可靠性提供了一个有希望的方法,可能减少诊断错误和治疗延迟.