  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.
