Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm

A common gynecological disease in the world is breast cancer that early diagnosis of this disease can be very effective in its treatment. The use of image processing methods and pattern recognition techniques in automatic breast detection from mammographic images decreases human errors and increment...

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Autores principales:	Xiuzhen Cai, Xia Li, Navid Razmjooy, Noradin Ghadimi
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/ea81c72f3b3243ad844a9e43c53c2347
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spelling	oai:doaj.org-article:ea81c72f3b3243ad844a9e43c53c23472021-11-22T01:10:06ZBreast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm1748-671810.1155/2021/5595180https://doaj.org/article/ea81c72f3b3243ad844a9e43c53c23472021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5595180https://doaj.org/toc/1748-6718A common gynecological disease in the world is breast cancer that early diagnosis of this disease can be very effective in its treatment. The use of image processing methods and pattern recognition techniques in automatic breast detection from mammographic images decreases human errors and increments the rapidity of diagnosis. In this paper, mammographic images are analyzed using image processing techniques and a pipeline structure for the diagnosis of the cancerous masses. In the first stage, the quality of mammogram images and the contrast of abnormal areas in the image are improved by using image contrast improvement and a noise decline. A method based on color space is then used for image segmentation that is followed by mathematical morphology. Then, for feature image extraction, a combined gray-level cooccurrence matrix (GLCM) and discrete wavelet transform (DWT) method is used. At last, a new optimized version of convolutional neural network (CNN) and a new improved metaheuristic, called Advanced Thermal Exchange Optimizer, are used for the classification of the features. A comparison of the simulations of the proposed technique with three different techniques from the literature applied on the MIAS mammogram database is performed to show its superiority. Results show that the accuracy of diagnosing cancer cases for the proposed method and applied on the MIAS database is 93.79%, and sensitivity and specificity are obtained 96.89% and 67.7%, respectively.Xiuzhen CaiXia LiNavid RazmjooyNoradin GhadimiHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Xiuzhen Cai Xia Li Navid Razmjooy Noradin Ghadimi Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm
description	A common gynecological disease in the world is breast cancer that early diagnosis of this disease can be very effective in its treatment. The use of image processing methods and pattern recognition techniques in automatic breast detection from mammographic images decreases human errors and increments the rapidity of diagnosis. In this paper, mammographic images are analyzed using image processing techniques and a pipeline structure for the diagnosis of the cancerous masses. In the first stage, the quality of mammogram images and the contrast of abnormal areas in the image are improved by using image contrast improvement and a noise decline. A method based on color space is then used for image segmentation that is followed by mathematical morphology. Then, for feature image extraction, a combined gray-level cooccurrence matrix (GLCM) and discrete wavelet transform (DWT) method is used. At last, a new optimized version of convolutional neural network (CNN) and a new improved metaheuristic, called Advanced Thermal Exchange Optimizer, are used for the classification of the features. A comparison of the simulations of the proposed technique with three different techniques from the literature applied on the MIAS mammogram database is performed to show its superiority. Results show that the accuracy of diagnosing cancer cases for the proposed method and applied on the MIAS database is 93.79%, and sensitivity and specificity are obtained 96.89% and 67.7%, respectively.
format	article
author	Xiuzhen Cai Xia Li Navid Razmjooy Noradin Ghadimi
author_facet	Xiuzhen Cai Xia Li Navid Razmjooy Noradin Ghadimi
author_sort	Xiuzhen Cai
title	Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm
title_short	Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm
title_full	Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm
title_fullStr	Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm
title_full_unstemmed	Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm
title_sort	breast cancer diagnosis by convolutional neural network and advanced thermal exchange optimization algorithm
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/ea81c72f3b3243ad844a9e43c53c2347
work_keys_str_mv	AT xiuzhencai breastcancerdiagnosisbyconvolutionalneuralnetworkandadvancedthermalexchangeoptimizationalgorithm AT xiali breastcancerdiagnosisbyconvolutionalneuralnetworkandadvancedthermalexchangeoptimizationalgorithm AT navidrazmjooy breastcancerdiagnosisbyconvolutionalneuralnetworkandadvancedthermalexchangeoptimizationalgorithm AT noradinghadimi breastcancerdiagnosisbyconvolutionalneuralnetworkandadvancedthermalexchangeoptimizationalgorithm
_version_	1718418435269984256

Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm

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