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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Kowsar
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4632564/ https://www.ncbi.nlm.nih.gov/pubmed/26557265 http://dx.doi.org/10.5812/iranjradiol.11656 |
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author | Yousefi Banaem, Hossein Mehri Dehnavi, Alireza Shahnazi, Makhtum |
author_facet | Yousefi Banaem, Hossein Mehri Dehnavi, Alireza Shahnazi, Makhtum |
author_sort | Yousefi Banaem, Hossein |
collection | PubMed |
description | 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 Co-occurrence 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 gave 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. |
format | Online Article Text |
id | pubmed-4632564 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Kowsar |
record_format | MEDLINE/PubMed |
spelling | pubmed-46325642015-11-09 Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data Yousefi Banaem, Hossein Mehri Dehnavi, Alireza Shahnazi, Makhtum Iran J Radiol Women's Imaging 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 Co-occurrence 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 gave 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. Kowsar 2015-07-22 /pmc/articles/PMC4632564/ /pubmed/26557265 http://dx.doi.org/10.5812/iranjradiol.11656 Text en Copyright © 2015, Tehran University of Medical Sciences and Iranian Society of Radiology. http://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited. |
spellingShingle | Women's Imaging Yousefi Banaem, Hossein Mehri Dehnavi, Alireza Shahnazi, Makhtum Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data |
title | Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data |
title_full | Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data |
title_fullStr | Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data |
title_full_unstemmed | Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data |
title_short | Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data |
title_sort | ensemble supervised classification method using the regions of interest and grey level co-occurrence matrices features for mammograms data |
topic | Women's Imaging |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4632564/ https://www.ncbi.nlm.nih.gov/pubmed/26557265 http://dx.doi.org/10.5812/iranjradiol.11656 |
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