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Breast Cancer Recognition Using a Novel Hybrid Intelligent Method

Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. This paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. The proposed method includes three mai...

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Detalles Bibliográficos
Autores principales: Addeh, Jalil, Ebrahimzadeh, Ata
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3632047/
https://www.ncbi.nlm.nih.gov/pubmed/23626945
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author Addeh, Jalil
Ebrahimzadeh, Ata
author_facet Addeh, Jalil
Ebrahimzadeh, Ata
author_sort Addeh, Jalil
collection PubMed
description Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. This paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. The proposed method includes three main modules: the feature extraction module, the classifier module, and the optimization module. In the feature extraction module, fuzzy features are proposed as the efficient characteristic of the patterns. In the classifier module, because of the promising generalization capability of support vector machines (SVM), a SVM-based classifier is proposed. In support vector machine training, the hyperparameters have very important roles for its recognition accuracy. Therefore, in the optimization module, the bees algorithm (BA) is proposed for selecting appropriate parameters of the classifier. The proposed system is tested on Wisconsin Breast Cancer database and simulation results show that the recommended system has a high accuracy.
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spelling pubmed-36320472013-04-26 Breast Cancer Recognition Using a Novel Hybrid Intelligent Method Addeh, Jalil Ebrahimzadeh, Ata J Med Signals Sens Original Article Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. This paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. The proposed method includes three main modules: the feature extraction module, the classifier module, and the optimization module. In the feature extraction module, fuzzy features are proposed as the efficient characteristic of the patterns. In the classifier module, because of the promising generalization capability of support vector machines (SVM), a SVM-based classifier is proposed. In support vector machine training, the hyperparameters have very important roles for its recognition accuracy. Therefore, in the optimization module, the bees algorithm (BA) is proposed for selecting appropriate parameters of the classifier. The proposed system is tested on Wisconsin Breast Cancer database and simulation results show that the recommended system has a high accuracy. Medknow Publications & Media Pvt Ltd 2012 /pmc/articles/PMC3632047/ /pubmed/23626945 Text en Copyright: © Journal of Medical Signals and Sensors http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Addeh, Jalil
Ebrahimzadeh, Ata
Breast Cancer Recognition Using a Novel Hybrid Intelligent Method
title Breast Cancer Recognition Using a Novel Hybrid Intelligent Method
title_full Breast Cancer Recognition Using a Novel Hybrid Intelligent Method
title_fullStr Breast Cancer Recognition Using a Novel Hybrid Intelligent Method
title_full_unstemmed Breast Cancer Recognition Using a Novel Hybrid Intelligent Method
title_short Breast Cancer Recognition Using a Novel Hybrid Intelligent Method
title_sort breast cancer recognition using a novel hybrid intelligent method
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3632047/
https://www.ncbi.nlm.nih.gov/pubmed/23626945
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