使用生物阻抗光谱和分数顺序电路建模来对肺癌组织进行分类
Masoomeh Ashoorirad1, Mina Ghadimi1, Raheleh Davoodi2
1Department of Biomedical Engineering, Hamedan University of Technology, Hamedan, Iran.
Physical and engineering sciences in medicine
|February 5, 2026
概括
这项研究引入了一种新的分数顺序电路模型,用于使用生物阻抗光谱检测肺癌. 平行分数科尔模型提高了诊断准确性,用于实时瘤识别.
科学领域:
- 生物医学工程 生物医学工程
- 在瘤学瘤学.
- 电气工程 电气工程
背景情况:
- 肺癌需要先进的检测方法.
- 生物阻抗光谱 (BIS) 显示了区分健康和恶性肺组织的潜力.
- 之前的BIS研究受到依赖原始阻抗数据的限制.
研究的目的:
- 通过BIS开发一种使用分数顺序电路建模的新型框架,用于通过BIS进行增强的肺组织分析.
- 提取生理学上相关的特征,以改善肺癌检测.
- 评估一种新的并行分数科尔 (PFC) 模型的诊断实用性.
主要方法:
- 在50kHz至5MHz的频率范围内,对328个肺组织样本进行了ex-vivo BIS测量.
- 八个电路模型,包括经典的科尔和拟议的PFC模型,被安装在阻抗数据上.
- 一个支持矢量机 (SVM) 分类器使用从PFC模型中提取的特征进行训练.
主要成果:
- 与其他模型相比,PFC模型表现出优越的分类性能.
- 在SVM分类器实现了90.00%的准确性,93.33%的灵敏度和86.67%的特异性,在区分瘤和健康组织.
- PFC模型的并行架构有效地捕获了瘤微观结构的异质性.
结论:
- 分数顺序模型,特别是PFC模型,在BIS中提供了超越曲线匹配精度的增强诊断实用性.
- PFC模型为手术期间实时,非侵入性肺结节局部化提供了一个有希望的途径.
- 这种方法提升了BIS在准确检测和描述肺癌方面的潜力.
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