Implementing combined neural network model for breast cancer diagnosis

  • I. Guler Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University
  • E. D. Ubeyli Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji Universitesi
Keywords: Combined neural network (CNN), Levenberg-Marquardt algorithm, cancer diagnosis, Diagnostic accuracy

Abstract

This paper illustrates the use of combined neural network (CNN) models to guide model selection for breast cancer diagnosis. Diagnosis tasks are among the most interesting activities in which to implement intelligent systems. Specifically, diagnosis is an attempt to accurately forecast the outcome of a specific situation, using as input information obtained from a concrete set of variables that potentially describe the situation. The CNN network model trained with Levenberg-Marquardt algorithm used the attributes of each record in the Wisconsin breast cancer database. The first level networks were implemented for the diagnosis of breast cancer using the attributes of each record as inputs. To improve diagnostic accuracy, the second level networks were trained using the outputs of the first level networks as input data. For the Wisconsin breast cancer diagnosis problem, the obtained total classification accuracy by the CNN network model was 98.15%. The CNN network model achieved accuracy rates which were higher than that of the stand-alone neural network models.

References

D. West, V. West, Improving diagnostic accuracy using a hierarchical neural network to model decision subtasks, International Journal of Medical Informatics, 57(1), 41-55, 2000.
E.D. Ubeyli, I. Guler, Improving medical diagnostic accuracy of ultrasound Doppler signals by combining neural network models, Computers in Biology and Medicine, 35(6), 533-554, 2005.
I. Guler, E.D. Ubeyli, ECG beat classifier designed by combined neural network model, Pattern Recognition, 38(2), 199-208, 2005.
D. West, V. West, Model selection for a medical diagnostic decision support system: a breast cancer detection case, Artificial Intelligence in Medicine, 20(3), 183-204, 2000.
R. Setiono, Extracting rules from pruned neural networks for breast cancer diagnosis, Artificial Intelligence in Medicine, 8 (1), 37-51, 1996.
R. Setiono, Generating concise and accurate classification rules for breast cancer diagnosis, Artificial Intelligence in Medicine, 18(3), 205-219, 2000.
W.H. Wolberg, O.L. Mangasarian, Multisurface method of pattern separation for medical diagnosis applied to breast cytology, Proceedings of the National Academy of Sciences, 87, 9193-9196, Washington, December 1990.
D.H. Wolpert, Stacked generalization, Neural Networks, 5, 241-259, 1992.
Published
2018-05-31
How to Cite
Guler, I., & Ubeyli, E. (2018). Implementing combined neural network model for breast cancer diagnosis. Bulletin of the International Scientific Surgical Association, 1(2), 26-28. Retrieved from http://surgjournal.ru/index.php/BISSA/article/view/73
Section
Original Articles