The accurate detection of early breast cancer is of great significance to each patient. In recent years, breast cancer non-invasive detection technology based on Ultra-Wideband (UWB) microwave has been proposed and developed extensively, which is complementary to the existing methods. In this paper, a novel approach is proposed for tumor existence detection based on feature extraction algorithm. Firstly, the breast features are obtained by Ensemble Empirical Mode Decomposition (EEMD) and valid correlation Intrinsic Mode Function (IMF) selection. Secondly, raw feature datasets are constructed and then simplified by Principal Component Analysis (PCA) or Recursive Feature Elimination (RFE). Finally, the detection is realized by Support Vector Machines (SVM). The influence of different kernel functions and feature selection methods on detection results is compared. In this study, 11,232 sets of backscatter signals from simulation results of four different categories’ breast models are utilized. And feature dataset is constructed by 24 specific features from each signal’s four valid components. The results demonstrate that the proposed method can extract representative features and detect the early breast cancer effectively with the accuracy of 84.8%.

Graphical abstract

中文翻译：

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使用基于 EEMD 的新型 UWB 微波特征提取方法精确检测早期乳腺肿瘤

早期乳腺癌的准确检测对每一位患者都具有重要意义。近年来，基于超宽带（UWB）微波的乳腺癌无创检测技术被广泛提出和发展，是对现有方法的补充。本文提出了一种基于特征提取算法的肿瘤存在检测新方法。首先，通过集合经验模态分解（EEMD）和有效相关内在模态函数（IMF）选择获得乳房特征。其次，构建原始特征数据集，然后通过主成分分析（PCA）或递归特征消除（RFE）进行简化。最后，通过支持向量机（SVM）实现检测。比较了不同核函数和特征选择方法对检测结果的影响。在这项研究中，使用了来自四种不同类别乳房模型的模拟结果的 11,232 组反向散射信号。特征数据集由来自每个信号的四个有效分量的 24 个特定特征构成。结果表明，所提方法能够提取代表性特征，有效检测早期乳腺癌，准确率为84.8%。

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