AI Article Synopsis
- Breast cancer poses a significant threat to women's health, making early detection and diagnosis crucial for lowering mortality rates.
- A novel selective ensemble method combines KNN, SVM, and Naive Bayes techniques, utilizing both ultrasound and mammography images for diagnosis.
- The proposed method achieved an accuracy of 88.73% and a sensitivity of 97.06%, demonstrating its effectiveness in diagnosing breast cancer and offering a new approach for selecting base classifiers in ensemble learning.
Article 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 presents a new way to choose the base classifier for ensemble learning.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5504929 | PMC |
| http://dx.doi.org/10.1155/2017/4896386 | DOI Listing |
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