Microwave breast cancer detection using Empirical Mode Decomposition features

TL;DR

This paper introduces an EMD-based feature extraction method for microwave breast cancer detection, demonstrating improved robustness and performance over traditional PCA-based methods through clinical and simulated data analysis.

Contribution

It proposes a novel EMD-based feature extraction approach that enhances robustness and detection accuracy in microwave breast cancer detection.

Findings

EMD features improve detection robustness against signal misalignment

Combined EMD and PCA features enhance classifier performance

Experimental results validate the effectiveness of the proposed method

Abstract

Microwave-based breast cancer detection has been proposed as a complementary approach to compensate for some drawbacks of existing breast cancer detection techniques. Among the existing microwave breast cancer detection methods, machine learning-type algorithms have recently become more popular. These focus on detecting the existence of breast tumours rather than performing imaging to identify the exact tumour position. A key step of the machine learning approaches is feature extraction. One of the most widely used feature extraction method is principle component analysis (PCA). However, it can be sensitive to signal misalignment. This paper presents an empirical mode decomposition (EMD)-based feature extraction method, which is more robust to the misalignment. Experimental results involving clinical data sets combined with numerically simulated tumour responses show that combined features from EMD and PCA improve the detection performance with an ensemble selection-based classifier.