A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis

Breast cancer has been one of the main diseases that threatens women's life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagn...

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Detalles Bibliográficos
Autores principales: Cong, Jinyu, Wei, Benzheng, He, Yunlong, Yin, Yilong, Zheng, Yuanjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5504929/
https://www.ncbi.nlm.nih.gov/pubmed/28740541
http://dx.doi.org/10.1155/2017/4896386
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author Cong, Jinyu
Wei, Benzheng
He, Yunlong
Yin, Yilong
Zheng, Yuanjie
author_facet Cong, Jinyu
Wei, Benzheng
He, Yunlong
Yin, Yilong
Zheng, Yuanjie
author_sort Cong, Jinyu
collection PubMed
description Breast cancer has been one of the main diseases that threatens women's life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator R presents a new way to choose the base classifier for ensemble learning.
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spelling pubmed-55049292017-07-24 A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis Cong, Jinyu Wei, Benzheng He, Yunlong Yin, Yilong Zheng, Yuanjie Comput Math Methods Med Research Article Breast cancer has been one of the main diseases that threatens women's life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator R presents a new way to choose the base classifier for ensemble learning. Hindawi 2017 2017-06-27 /pmc/articles/PMC5504929/ /pubmed/28740541 http://dx.doi.org/10.1155/2017/4896386 Text en Copyright © 2017 Jinyu Cong et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Cong, Jinyu
Wei, Benzheng
He, Yunlong
Yin, Yilong
Zheng, Yuanjie
A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis
title A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis
title_full A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis
title_fullStr A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis
title_full_unstemmed A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis
title_short A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis
title_sort selective ensemble classification method combining mammography images with ultrasound images for breast cancer diagnosis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5504929/
https://www.ncbi.nlm.nih.gov/pubmed/28740541
http://dx.doi.org/10.1155/2017/4896386
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