Author/Authors :
Cong, Jinyu Shandong Normal University - Jinan, China , Wei, Benzheng Shandong University of Traditional Chinese Medicine - Jinan, China , He, Yunlong Shandong Normal University - Jinan, China , Yin, Yilong School of Computer Science and Technology - Shandong University - Jinan, China , Zheng, Yuanjie Shandong Normal University - Jinan, China
Abstract :
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.
Keywords :
Mammography , KNN , Ultrasound , SVM
