Open AccessAutomatic recognition of breast invasive ductal carcinoma based on terahertz spectroscopy with wavelet packet transform and machine learning
Author(s) -
Wenquan Liu,
Rui Zhang,
Ling Yu,
Haixiong Tang,
Rongbin She,
Guanglu Wei,
Xiaojing Gong,
Yuanfu Lu
Publication year - 2020
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.381623
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , wavelet packet decomposition , principal component analysis , wavelet transform , ductal carcinoma , feature extraction , wavelet , classifier (uml) , terahertz radiation , breast cancer , machine learning , speech recognition , physics , medicine , optics , cancer
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.