Classification of micro-calcification in mammograms using scalable linear Fisher discriminant analysis.
Breast cancer is one of the major causes of death in women. Computer Aided Diagnosis (CAD) systems are being developed to assist radiologists in early diagnosis. Micro-calcifications can be an early symptom of breast cancer. Besides detection, classification of micro-calcification as benign or malig...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 8; pp. 1475 - 1486 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2018
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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=130773116&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130773116 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2018 vid: 56 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 130773116 130773116 NLM29368264 10.1007/s11517-017-1774-z NLM29368264 130773116 ppf: 1475 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of micro-calcification in mammograms using scalable linear Fisher discriminant analysis. aug: au: Suhail, Zobia Denton, Erika R. E. Zwiggelaar, Reyer affil: Aberystwyth University, Aberystwyth, UK sug: subj: Mammography Methods Calcinosis Classification Female Databases Factor Analysis Discriminant Analysis Female ab: Breast cancer is one of the major causes of death in women. Computer Aided Diagnosis (CAD) systems are being developed to assist radiologists in early diagnosis. Micro-calcifications can be an early symptom of breast cancer. Besides detection, classification of micro-calcification as benign or malignant is essential in a complete CAD system. We have developed a novel method for the classification of benign and malignant micro-calcification using an improved Fisher Linear Discriminant Analysis (LDA) approach for the linear transformation of segmented micro-calcification data in combination with a Support Vector Machine (SVM) variant to classify between the two classes. The results indicate an average accuracy equal to 96% which is comparable to state-of-the art methods in the literature. Graphical Abstract Classification of Micro-calcification in Mammograms using Scalable Linear Fisher Discriminant Analysis. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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