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适应性Bandelet转换和转移学习为几何意识的甲状腺癌超声波分类.

Yassine Habchi1, Hamza Kheddar2, Mohamed Chahine Ghanem3

  • 1Faculty of Technology, University Salhi Ahmed, Naama 45000, Algeria.

Diagnostics (Basel, Switzerland)
|February 27, 2026
PubMed
概括
此摘要是机器生成的。

这项研究提高了甲状腺结节的分类,使用了适应几何的Bandelet Transform (BT) 和转移学习 (TL). 综合方法显著提高了超声波图像分析的准确性和效率.

关键词:
班德莱特变形 变形 变形深度学习是一种深度学习.诊断 诊断 诊断 的 诊断 诊断 诊断 诊断 的 诊断医学成像医学成像甲状腺癌是一种癌症.转移学习转移学习

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 由于数据有限和纹理复杂性,在超声波中对甲状腺结节进行分类是困难的.
  • 传统的方法在超声波图像中难以捕捉复杂的,多方向的纹理.

研究的目的:

  • 为了提高数据效率的甲状腺结节分类.
  • 通过使用Bandelet转换 (BT) 和转移学习 (TL) 来增强特征表示和概括.

主要方法:

  • 应用了适应几何的带列转换 (BT) 来增强方向和结构编码.
  • 通过SMOTE减轻了阶级不平衡,并通过增强增加了数据多样性.
  • 使用 ImageNet 预训练架构进行分类的特征,其中 VGG19 显示出最佳性能.

主要成果:

  • 在各种值的波段表示上,BT预处理提高了性能.
  • 该BT+TL (VGG19) 模型实现了98.91%的精度,98.11%的灵敏度,97.31%的特异性和98.89%的F1分数.
  • 在DDTI数据集上表现优于可比方法.

结论:

  • 将几何适应变换与TL骨干相结合,为甲状腺结节分类提供了一个强大的,数据效率高的策略.
  • 这种方法在有限的注释和复杂的纹理方面尤其有效.
  • 项目和代码是公开的.