Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network

Breast cancer is the second most common cause of cancer-related deaths among women. Early detection leads to better prognosis and saves lives. The 5-year survival rate of breast cancer is 99% if it is located only in breast. Conventional computer-aided diagnosis (CADx) systems for breast...

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Autores principales:	Hasan Nasir Khan, Ahmad Raza Shahid, Basit Raza, Amir Hanif Dar, Hani Alquhayz
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2019
Materias:	Breast cancer classification computer-aided diagnosis deep learning mammogram multi-view Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/fb4f64423fcd474abe26c2ae4cf16021
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spelling	oai:doaj.org-article:fb4f64423fcd474abe26c2ae4cf160212021-11-19T00:02:51ZMulti-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network2169-353610.1109/ACCESS.2019.2953318https://doaj.org/article/fb4f64423fcd474abe26c2ae4cf160212019-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/8897609/https://doaj.org/toc/2169-3536Breast cancer is the second most common cause of cancer-related deaths among women. Early detection leads to better prognosis and saves lives. The 5-year survival rate of breast cancer is 99% if it is located only in breast. Conventional computer-aided diagnosis (CADx) systems for breast cancer use the single view information of mammograms to assist the radiologists. More recent work has focused on more than one views. Existing multi-view based CADx systems normally employ only two views namely Cranio-Caudal (CC) and Medio-Lateral-Oblique (MLO). The information fusion of the two views proved the effectiveness of the system for mammogram classification which cannot be achieved by single view information. However, combining the information of four views of mammograms increases the performance of classification. In this study, we propose Multi-View Feature Fusion (MVFF) based CADx system using feature fusion technique of four views for classification of mammogram. The complete CADx tool contains three stages, the first stage have the ability to classify mammogram into abnormal or normal, second stage is about classification of mass or calcification and in the final stage classification of malignant or benign classification is performed. Convolutional Neural Network (CNN) based feature extraction models operate on each view separately. These extracted features were fused into one final layer for ultimate prediction. Our proposed system is trained on four views of mammograms, after data augmentation. We performed our experiments on publicly available datasets such as CBIS-DDSM (Curated Breast Imaging Subset of DDSM) and mini-MIAS database of mammograms. In comparison with literature the MVFF based system is performed better than a single view-based system for mammogram classification. We have achieved area under ROC curve (AUC) of 0.932 for mass and calcification and 0.84 for malignant and benign, which is higher than all single-view based systems. The value of AUC for normal and abnormal classification is 0.93.Hasan Nasir KhanAhmad Raza ShahidBasit RazaAmir Hanif DarHani AlquhayzIEEEarticleBreast cancerclassificationcomputer-aided diagnosisdeep learningmammogrammulti-viewElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 7, Pp 165724-165733 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Breast cancer classification computer-aided diagnosis deep learning mammogram multi-view Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Breast cancer classification computer-aided diagnosis deep learning mammogram multi-view Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hasan Nasir Khan Ahmad Raza Shahid Basit Raza Amir Hanif Dar Hani Alquhayz Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network
description	Breast cancer is the second most common cause of cancer-related deaths among women. Early detection leads to better prognosis and saves lives. The 5-year survival rate of breast cancer is 99% if it is located only in breast. Conventional computer-aided diagnosis (CADx) systems for breast cancer use the single view information of mammograms to assist the radiologists. More recent work has focused on more than one views. Existing multi-view based CADx systems normally employ only two views namely Cranio-Caudal (CC) and Medio-Lateral-Oblique (MLO). The information fusion of the two views proved the effectiveness of the system for mammogram classification which cannot be achieved by single view information. However, combining the information of four views of mammograms increases the performance of classification. In this study, we propose Multi-View Feature Fusion (MVFF) based CADx system using feature fusion technique of four views for classification of mammogram. The complete CADx tool contains three stages, the first stage have the ability to classify mammogram into abnormal or normal, second stage is about classification of mass or calcification and in the final stage classification of malignant or benign classification is performed. Convolutional Neural Network (CNN) based feature extraction models operate on each view separately. These extracted features were fused into one final layer for ultimate prediction. Our proposed system is trained on four views of mammograms, after data augmentation. We performed our experiments on publicly available datasets such as CBIS-DDSM (Curated Breast Imaging Subset of DDSM) and mini-MIAS database of mammograms. In comparison with literature the MVFF based system is performed better than a single view-based system for mammogram classification. We have achieved area under ROC curve (AUC) of 0.932 for mass and calcification and 0.84 for malignant and benign, which is higher than all single-view based systems. The value of AUC for normal and abnormal classification is 0.93.
format	article
author	Hasan Nasir Khan Ahmad Raza Shahid Basit Raza Amir Hanif Dar Hani Alquhayz
author_facet	Hasan Nasir Khan Ahmad Raza Shahid Basit Raza Amir Hanif Dar Hani Alquhayz
author_sort	Hasan Nasir Khan
title	Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network
title_short	Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network
title_full	Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network
title_fullStr	Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network
title_full_unstemmed	Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network
title_sort	multi-view feature fusion based four views model for mammogram classification using convolutional neural network
publisher	IEEE
publishDate	2019
url	https://doaj.org/article/fb4f64423fcd474abe26c2ae4cf16021
work_keys_str_mv	AT hasannasirkhan multiviewfeaturefusionbasedfourviewsmodelformammogramclassificationusingconvolutionalneuralnetwork AT ahmadrazashahid multiviewfeaturefusionbasedfourviewsmodelformammogramclassificationusingconvolutionalneuralnetwork AT basitraza multiviewfeaturefusionbasedfourviewsmodelformammogramclassificationusingconvolutionalneuralnetwork AT amirhanifdar multiviewfeaturefusionbasedfourviewsmodelformammogramclassificationusingconvolutionalneuralnetwork AT hanialquhayz multiviewfeaturefusionbasedfourviewsmodelformammogramclassificationusingconvolutionalneuralnetwork
_version_	1718420705290223616

Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network

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