使用微波数据集的机器学习算法对癌症组织进行分类.
Rabia Toprak1, Huseyin Duysak1, Zeliha Esin Celik2
1Department of Electrical-Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey.
Bioelectromagnetics
|January 6, 2026
概括
微波测量可以快速区分癌症与健康的结肠组织. k-最近邻近算法实现了最高的准确性,加速了癌症诊断.
科学领域:
- 生物工程是生物工程.
- 医学物理 医学物理
- 计算生物学 计算生物学
背景情况:
- 癌症发病率上升需要更快的诊断方法.
- 传统的病理学报告是耗时的.
- 早期发现癌症对于治疗成功至关重要.
研究的目的:
- 开发一种快速的方法来区分癌症与健康的结肠组织.
- 评估用于病理组织分析的微波测量技术.
- 为了比较组织识别准确性的分类算法.
主要方法:
- 在结肠组织样本上利用自由空间微波测量 (18-26 GHz).
- 收集的散射参数,包括反射和传播系数.
- 训练并测试了k-最近邻居 (KNN),人工神经网络 (ANN) 和支持矢量机器 (SVM) 算法.
主要成果:
- 使用各种测量特征组合创建了四个不同的数据集.
- 该KNN算法证明了最高的分类准确性.
- 使用反射,传输系数和频率数据实现了最佳性能.
结论:
- 微波测量为癌症诊断提供了一个有希望的,加快的方法.
- 根据微波数据,KNN算法有效地对结肠组织进行分类.
- 这种技术有可能显著减少诊断周转时间.
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