Abstract
We demonstrate an automatic recognition strategy for terahertz (THz) pulsed signals of breast invasive ductal carcinoma (IDC) based on a wavelet entropy feature extraction and a machine learning classifier. The wavelet packet transform was implemented into the complexity analysis of the transmission THz signal from a breast tissue sample. A novel index of energy to Shannon entropy ratio (ESER) was proposed to distinguish different tissues. Furthermore, the principal component analysis (PCA) method and machine learning classifier were further adopted and optimized for automatic classification of the THz signal from breast IDC sample. The areas under the receiver operating characteristic curves are all larger than 0.89 for the three adopted classifiers. The best breast IDC recognition performance is with the precision, sensitivity and specificity of 92.85%, 89.66% and 96.67%, respectively. The results demonstrate the effectiveness of the ESER index together with the machine learning classifier for automatically identifying different breast tissues.
Highlights
Terahertz time-domain spectroscopy (THz-TDS) has emerged as a promising technique to characterize the properties of different biological tissues, because THz radiation is non-ionizing and highly sensitive to water and macromolecules [1,2]
We propose an automatic recognition strategy for transmission THz pulsed signal of breast invasive ductal carcinoma (IDC) based on wavelet entropy feature extraction and machine learning classifier
With the addition of the extra principal components (PCs), the total accuracy gradually drops for the k-nearest neighbor (kNN) classifier, and it tends to saturation for the Ensemble classifier with subspace kNN strategy
Summary
Terahertz time-domain spectroscopy (THz-TDS) has emerged as a promising technique to characterize the properties of different biological tissues, because THz radiation is non-ionizing and highly sensitive to water and macromolecules [1,2]. Different types of tissues such as breast cancer, gastric tumor, brain glioma, and oral cancer have been detected using THz-TDS [3,4,5,6]. The majority of the patients will conduct the lumpectomy to ensure that the cancer area together with a minimal margin of normal tissue are totally removed [8]. Various imaging techniques such as optical coherence tomography, Raman imaging and phase shifting interferometry have been adopted for breast cancer detection [9,10,11]. THz-TDS has demonstrated great potential to differentiate the tumor region in breast tissue from the lumpectomy, which has attracted increasing interest recently [3,8,12,13,14]
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