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Updated: Feb 26, 2026
Clinical Imaging of Microwave Mammography
Published on: November 14, 2025
A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer
Jinyu Cong1, Benzheng Wei2, Yunlong He1
1School of Information Science and Engineering, Key Lab of Intelligent Computing & Information Security in Universities of Shandong, Institute of Life Sciences, Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, and Key Lab of Intelligent Information Processing, Shandong Normal University, Jinan 250358, China.
This study introduces a new method for early breast cancer detection using combined ultrasound and mammography images. The selective ensemble model achieved high accuracy and sensitivity, improving diagnostic efficiency.
Area of Science:
- Medical Imaging
- Machine Learning
- Oncology
Background:
- Breast cancer poses a significant threat to women's health.
- Early detection and diagnosis are crucial for reducing mortality rates.
Purpose of the Study:
- To propose a selective ensemble method for breast cancer diagnosis.
- To integrate ultrasound and mammography images for improved diagnostic accuracy.
Main Methods:
- Utilized a selective ensemble method combining K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Naive Bayes classifiers.
- Developed an indicator 'R' for optimal base classifier selection in ensemble learning.
Main Results:
- Achieved an accuracy of 88.73% for breast cancer diagnosis.
- Demonstrated a high sensitivity rate of 97.06%.
Conclusions:
- The proposed selective ensemble method is efficient for breast cancer diagnosis.
- The indicator 'R' offers a novel approach for selecting base classifiers in ensemble models.
