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 component 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 proposes feature extraction methods based on time-frequency representations of microwave data, including the wavelet transform and the empirical mode decomposition. Time-invariant statistics can be generated to provide features more robust to data misalignment. We validate results using clinical data sets combined with numerically simulated tumour responses. Experimental results show that features extracted from decomposition results of the wavelet transform and EMD improve the detection performance when combined with an ensemble selection-based classifier.