Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data.
Background: Breast cancer is one of the most encountered cancers in women. Detection and classification of the cancer into malignant or benign is one of the challenging fields of the pathology. Objectives: Our aim was to classify the mammogram data into normal and abnormal by ensemble classification...
| Publicado en: | Iranian Journal of Radiology Vol. 12; no. 3; pp. 1 - 9 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Tehran University of Medical Sciences
Jul2015
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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=108943736&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 108943736 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17351065 54ZL jtl: Iranian Journal of Radiology issn: 17351065 maglogo: N pubinfo: dt: Jul2015 vid: 12 iid: 3 pid: 21783 pub: Tehran University of Medical Sciences artinfo: ui: 108943736 108943736 108943736 10.5812/iranjradiol.11656 NLM26557265 108943736 ppf: 1 ppct: 8 formats: tig: atl: Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data. aug: au: Banaem, Hossein Yousefi Dehnavi, Alireza Mehri Shahnazi, Makhtum affil: Department of Biomedical Engineering, Faculty of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran sug: subj: Breast Neoplasms Classification Mammography Human Female Algorithms Sensitivity and Specificity Descriptive Statistics Diagnosis, Computer Assisted Breast Neoplasms Diagnosis Female ab: Background: Breast cancer is one of the most encountered cancers in women. Detection and classification of the cancer into malignant or benign is one of the challenging fields of the pathology. Objectives: Our aim was to classify the mammogram data into normal and abnormal by ensemble classification method. Patients and Methods: In this method, we first extract texture features from cancerous and normal breasts, using the Gray-Level Cooccurrence Matrices (GLCM) method. To obtain better results, we select a region of breast with high probability of cancer occurrence before feature extraction. After features extraction, we use the maximum difference method to select the features that have predominant difference between normal and abnormal data sets. Six selected features served as the classifying tool for classification purpose by the proposed ensemble supervised algorithm. For classification, the data were first classified by three supervised classifiers, and then by simple voting policy, we finalized the classification process. Results: After classification with the ensemble supervised algorithm, the performance of the proposed method was evaluated by perfect test method, which gaves the sensitivity and specificity of 96.66% and 97.50%, respectively. Conclusions: In this study, we proposed a new computer aided diagnostic tool for the detection and classification of breast cancer. The obtained results showed that the proposed method is more reliable in diagnostic to assist the radiologists in the detection of abnormal data and to improve the diagnostic accuracy. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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