Precise detection of early breast tumor using a novel EEMD-based feature extraction approach by UWB microwave.
The accurate detection of early breast cancer is of great significance to each patient. In recent years, breast cancer non-invasive detection technology based on Ultra-Wideband (UWB) microwave has been proposed and developed extensively, which is complementary to the existing methods. In this paper,...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 3; pp. 721 - 732 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149031283&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149031283 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2021 vid: 59 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 149031283 149031283 NLM33629221 10.1007/s11517-021-02339-5 NLM33629221 149031283 ppf: 721 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Precise detection of early breast tumor using a novel EEMD-based feature extraction approach by UWB microwave. aug: au: Liu, Guancong Xiao, Xia Song, Hang Kikkawa, Takamaro affil: Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, School of Microelectronics, Tianjin University, Tianjin, People's Republic of China sug: subj: Breast Neoplasms Microwaves Female Breast Algorithms Female ab: The accurate detection of early breast cancer is of great significance to each patient. In recent years, breast cancer non-invasive detection technology based on Ultra-Wideband (UWB) microwave has been proposed and developed extensively, which is complementary to the existing methods. In this paper, a novel approach is proposed for tumor existence detection based on feature extraction algorithm. Firstly, the breast features are obtained by Ensemble Empirical Mode Decomposition (EEMD) and valid correlation Intrinsic Mode Function (IMF) selection. Secondly, raw feature datasets are constructed and then simplified by Principal Component Analysis (PCA) or Recursive Feature Elimination (RFE). Finally, the detection is realized by Support Vector Machines (SVM). The influence of different kernel functions and feature selection methods on detection results is compared. In this study, 11,232 sets of backscatter signals from simulation results of four different categories' breast models are utilized. And feature dataset is constructed by 24 specific features from each signal's four valid components. The results demonstrate that the proposed method can extract representative features and detect the early breast cancer effectively with the accuracy of 84.8%. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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