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

被引：19

作者：

Cong, Jinyu ^{[1
,2
]}

Wei, Benzheng ^{[3
]}

He, Yunlong ^{[1
,2
]}

Yin, Yilong ^{[4
]}

Zheng, Yuanjie ^{[1
,2
]}

机构：

[1] Shandong Normal Univ, Sch Informat Sci & Engn, Key Lab Intelligent Comp & Informat Secur Univ Sh, Inst Life Sci,Shandong Prov Key Lab Distributed C, Jinan 250358, Peoples R China

[2] Shandong Normal Univ, Key Lab Intelligent Informat Proc, Jinan 250358, Peoples R China

[3] Shandong Univ Tradit Chinese Med, Coll Sci & Technol, Jinan 250014, Peoples R China

[4] Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, Peoples R China

来源：

COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE | 2017年 / 2017卷

关键词：

COMPUTER-AIDED DIAGNOSIS; PERFORMANCE; LESIONS;

D O I：

10.1155/2017/4896386

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：7

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