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Classification of terahertz pulsed signals from breast tissues using wavelet packet energy feature exaction and machine learning classifiers
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Paper Abstract

Here, we propose an effective classification strategy for THz pulsed signals of breast tissues based on wavelet packet energy (WPE) feature exaction and machine learning classifiers. The parafin-embedded breast tissue samples were adopted in this study and identified as tumor (226 samples), healthy fibrous tissue (233 samples) or adipose tissue (178 samples) based on the histological results. Firstly, the THz pulsed signals of tissue samples were acquired using a standard transmission THz time-domain spectrometer. Then, the signals were decomposed by the wavelet packet transform (WPT) and the features of the WPE were extracted. To reduce the dimensionality of extracted features, the principal components analysis (PCA) method was employed. Six different machine learning classifiers were then performed and compared for automatic classification of different tissue samples. The highest classification accuracy is up to 97% using the fine Gaussian support vector machine (SVM) approach. The results indicate that the WPE feature exaction combined with machine learning classifier can be used for automatic evaluation of biological tissue THz signals with good accuracy.

Paper Details

Date Published: 18 November 2019
PDF: 8 pages
Proc. SPIE 11196, Infrared, Millimeter-Wave, and Terahertz Technologies VI, 1119606 (18 November 2019); doi: 10.1117/12.2537277
Show Author Affiliations
Wenquan Liu, Shenzhen Institutes of Advanced Technology (China)
Biomedical Engineering Lab. for Photoelectric Sensing Technology (China)
Rui Zhang, Shenzhen Institutes of Advanced Technology (China)
Biomedical Engineering Lab. for Photoelectric Sensing Technology (China)
Yuanfu Lu, Shenzhen Institutes of Advanced Technology (China)
Biomedical Engineering Lab. for Photoelectric Sensing Technology (China)
Rongbin She, Shenzhen Institutes of Advanced Technology (China)
Biomedical Engineering Lab. for Photoelectric Sensing Technology (China)
Kai Zhou, Shenzhen Institutes of Advanced Technology (China)
Biomedical Engineering Lab. for Photoelectric Sensing Technology (China)
Beihua Fang, Shenzhen Institutes of Advanced Technology (China)
Biomedical Engineering Lab. for Photoelectric Sensing Technology (China)
Guanglu Wei, Shenzhen Institutes of Advanced Technology (China)
Biomedical Engineering Lab. for Photoelectric Sensing Technology (China)
Guangyuan Li, Shenzhen Institutes of Advanced Technology (China)
Biomedical Engineering Lab. for Photoelectric Sensing Technology (China)


Published in SPIE Proceedings Vol. 11196:
Infrared, Millimeter-Wave, and Terahertz Technologies VI
Cunlin Zhang; Xi-Cheng Zhang; Masahiko Tani, Editor(s)

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