Microwave breast cancer detection using time-frequency representations.
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. The...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 4; pp. 571 - 583 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2018
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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=128549168&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128549168 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2018 vid: 56 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 128549168 128549168 NLM28836083 10.1007/s11517-017-1712-0 NLM28836083 128549168 ppf: 571 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Microwave breast cancer detection using time-frequency representations. aug: au: Song, Hongchao Li, Yunpeng Men, Aidong affil: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China sug: subj: Breast Neoplasms Signal Processing, Computer Assisted Microwaves Therapeutic Use Breast ROC Curve Female Scales Female ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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