CFS-SMO based classification of breast density using multiple texture models.
It is highly acknowledged in the medical profession that density of breast tissue is a major cause for the growth of breast cancer. Increased breast density was found to be linked with an increased risk of breast cancer growth, as high density makes it difficult for radiologists to see an abnormalit...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 52; no. 6; pp. 521 - 530 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jun2014
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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=103823450&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103823450 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2014 vid: 52 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103823450 NLM24771118 2012583695 10.1007/s11517-014-1158-6 NLM24771118 103823450 ppf: 521 ppct: 9 formats: fmt: @attributes: type: P tig: atl: CFS-SMO based classification of breast density using multiple texture models. aug: au: Sharma, Vipul Singh, Sukhwinder affil: UIET, Panjab University, Chandigarh, India, vipuls85@gmail.com. sug: subj: Breast Physiology Image Processing, Computer Assisted Methods Mammography Classification Mammography Methods Information Science Methods Algorithms Probability Female Human Female ab: It is highly acknowledged in the medical profession that density of breast tissue is a major cause for the growth of breast cancer. Increased breast density was found to be linked with an increased risk of breast cancer growth, as high density makes it difficult for radiologists to see an abnormality which leads to false negative results. Therefore, there is need for the development of highly efficient techniques for breast tissue classification based on density. This paper presents a hybrid scheme for classification of fatty and dense mammograms using correlation-based feature selection (CFS) and sequential minimal optimization (SMO). In this work, texture analysis is done on a region of interest selected from the mammogram. Various texture models have been used to quantify the texture of parenchymal patterns of breast. To reduce the dimensionality and to identify the features which differentiate between breast tissue densities, CFS is used. Finally, classification is performed using SMO. The performance is evaluated using 322 images of mini-MIAS database. Highest accuracy of 96.46% is obtained for two-class problem (fatty and dense) using proposed approach. Performance of selected features by CFS is also evaluated by Naïve Bayes, Multilayer Perceptron, RBF Network, J48 and kNN classifier. The proposed CFS-SMO method outperforms all other classifiers giving a sensitivity of 100%. This makes it suitable to be taken as a second opinion in classifying breast tissue density. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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