Machine Learning Techniques for Classification of Breast Cancer-Reference-Cited by-同舟云学术

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Machine Learning Techniques for Classification of Breast Cancer

Published:2018-05-30 Issue: Volume: Page:197-200
ISSN:1680-0737
Container-title:IFMBE Proceedings
language:
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Author:

Osmanović Ahmed,Halilović Sabina,Ilah Layla Abdel,Fojnica Adnan,Gromilić Zehra

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-10-9035-6_35

Reference21 articles.

1. Schneider, A. P., Zainer, C. M., Kubat, C. K., Mullen, N. K., & Windisch, A. K. (2014). The breast cancer epidemic: 10 facts. The Linacre Quarterly, 81(3), 244–277.

2. Al-Hajj, M., Wicha, M. S., Benito-Hernandez, A., Morrison, S. J., & Clarke, M. F. (2003). Prospective identification of tumorigenic breast cancer cells. Proceedings of the National Academy of Sciences, 100(7), 3983–3988.

3. Sinn, H. P., & Kreipe, H. (2013). A brief overview of the WHO classification of breast tumors. Breast Care, 8(2), 149–154.

4. Osmanović, A., Abdel-Ilah, L., Hodžić, A., Kevric, J., & Fojnica, A. (2017). Ovary Cancer Detection using Decision Tree Classifiers based on Historical Data of Ovary Cancer Patients. In CMBEBIH 2017 (pp. 503–510). Springer, Singapore.

5. Bušatlić, E., Osmanović, A., Jakupović, A., Nuhić, J., & Hodžić, A. (2017). Using Neural Networks and Ensemble Techniques based on Decision Trees for Skin Permeability Prediction. In CMBEBIH 2017 (pp. 41–50). Springer, Singapore.

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