The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM

Breast cancer is one of the common cancers among women so that early diagnosing of it can effectively help its treatment in this study, considering combination of Mammography and MRI pictures, we will try to recognize glands in existing pictures which identify all around of gland complete and precis...

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Main Authors: Elham Gohariyan, Mansour Esmaeilpour, Mohammad Mehdi Shirmohammadi
Format: Article
Language:English
Published: Universidad Internacional de La Rioja (UNIR) 2017-08-01
Series:International Journal of Interactive Multimedia and Artificial Intelligence
Subjects:
Online Access:http://www.ijimai.org/journal/node/1492
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spelling doaj-7d6da1d2c0e94c49bd76a2894ddf985f2020-11-24T20:53:37ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16601989-16602017-08-0145202410.9781/ijimai.2017.454ijimai.2017.454The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVMElham GohariyanMansour EsmaeilpourMohammad Mehdi ShirmohammadiBreast cancer is one of the common cancers among women so that early diagnosing of it can effectively help its treatment in this study, considering combination of Mammography and MRI pictures, we will try to recognize glands in existing pictures which identify all around of gland complete and precisely and separate it completely. In this method using artificial intelligence algorithm such as Affine transformation, Gabor filter, neural network, and support vector machine, image analysis will be carried out. The accuracy of proposed method is 98.14. In this work a special framework is presented which simplifies cancer diagnosis. The algorithm of proposed method is tested on z16 images. High speed and lack of human error are the most important factors in proposed intelligent system.http://www.ijimai.org/journal/node/1492CancerMachine LearningMedicineNeural Network
collection DOAJ
language English
format Article
sources DOAJ
author Elham Gohariyan
Mansour Esmaeilpour
Mohammad Mehdi Shirmohammadi
spellingShingle Elham Gohariyan
Mansour Esmaeilpour
Mohammad Mehdi Shirmohammadi
The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM
International Journal of Interactive Multimedia and Artificial Intelligence
Cancer
Machine Learning
Medicine
Neural Network
author_facet Elham Gohariyan
Mansour Esmaeilpour
Mohammad Mehdi Shirmohammadi
author_sort Elham Gohariyan
title The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM
title_short The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM
title_full The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM
title_fullStr The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM
title_full_unstemmed The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM
title_sort combination of mammography and mri for diagnosing breast cancer using fuzzy nn and svm
publisher Universidad Internacional de La Rioja (UNIR)
series International Journal of Interactive Multimedia and Artificial Intelligence
issn 1989-1660
1989-1660
publishDate 2017-08-01
description Breast cancer is one of the common cancers among women so that early diagnosing of it can effectively help its treatment in this study, considering combination of Mammography and MRI pictures, we will try to recognize glands in existing pictures which identify all around of gland complete and precisely and separate it completely. In this method using artificial intelligence algorithm such as Affine transformation, Gabor filter, neural network, and support vector machine, image analysis will be carried out. The accuracy of proposed method is 98.14. In this work a special framework is presented which simplifies cancer diagnosis. The algorithm of proposed method is tested on z16 images. High speed and lack of human error are the most important factors in proposed intelligent system.
topic Cancer
Machine Learning
Medicine
Neural Network
url http://www.ijimai.org/journal/node/1492
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