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Journals / Istanbul University Journal of Electrical and Electronics Engineering / 2004 / Cilt: 4 - Sayı: 2

Breast cancer diagnosis using statistical neural networks

Tüba KIYAN
Pages
1149–1153
Publication Date
2022-10-18
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Statistical neural networks are used to increase the accuracy and objectivity of breast cancer diagnosis.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Breast cancer is the second largest cause of cancer deaths among women.
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Key Findings & Data:The performance of the statistical neural network structures, radial basis network (RBF), general regression neural network (GRNN) and probabilistic neural network (PNN) are examined on the Wisconsin breast cancer data (WBCD) in this paper.
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Conclusion & Impact:Statistical neural networks are used to increase the accuracy and objectivity of breast cancer diagnosis.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Breast cancer is the second largest cause of cancer deaths among women. The performance of the statistical neural network structures, radial basis network (RBF), general regression neural network (GRNN) and probabilistic neural network (PNN) are examined on the Wisconsin breast cancer data (WBCD) in this paper. This is a well-used database in machine learning, neural network and signal processing. Statistical neural networks are used to increase the accuracy and objectivity of breast cancer diagnosis.

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Cite This Article

KIYAN, T. (2022). Breast cancer diagnosis using statistical neural networks. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 4 - Sayı: 2, 1149–1153. https://ajindex.com/articles/543864

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References (1)

  1. Carlos Andres Pena-Reyes, Moshe Sipper, A fuzzy-genetic approach to breast cancer diagnosis, Artificial Intelligence in Medicine, 131–155, 1999 [2] of the LSA machine, ICONIP A. A. Albrecht, G. Lappas, S. A. Vinterbo, C.K. Wong, L. Ohno-Machado, Two applications 2002. [3] C. A. Pena-Reyes, M. Sipper, Fuzzy CoCo: A cooperative coevolutionary approach to fuzzy modeling, IEEE Transactions on Fuzzy Systems, 9(5):727-737, 2001. [4] R. Setiono, Generating concise and accurate classification rules for breast cancer diagnosis, Artificial Intelligence in Medicine, 18(3): 205-217, 2000. [5] J. Zhang, Selecting typical instances in instance-based learning, Proc. Ninth International Machine Learning Workshop, pp. 470-479, Morgan-Kaufmann, 1992. [6] Newton Cheung, Machine Learning Techniques for Medical Analysis, Thesis for the degree of Bachelor of Engineering, 2001. [7] S. Haykin, Neural Networks: A Comprehensive Foundation, Mac Millan College Publishing Company, 1994 [8] Anthony T. C. Goh, Probabilistic neural network for seismic liquefaction potential, NRC Research Press Web site, 2002 [9] C. Lu, J. De Brabanter, S. Van Huffel, I. Vergote, D. Timmerman, Using artificial neural networks to predict malignancy of ovarian tumors, 23rd Annual Int. Conf. Of the IEEE Engineering in Medicine and Biology Society.