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• W2715193206 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 <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M1><mml:mrow><mml:mi>R</mml:mi></mml:mrow></mml:math> presents a new way to choose the base classifier for ensemble learning." @default.
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• W2715193206 date "2017-01-01" @default.
• W2715193206 modified "2023-10-14" @default.
• W2715193206 title "A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis" @default.
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• W2715193206 doi "https://doi.org/10.1155/2017/4896386" @default.
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