Neural network based algorithms for diagnosis and classification of breast cancer tumor

Authors:
In-Sung Jung;Devinder Thapa;Gi-Nam Wang
Affiliations:
Department of Industrial and Information Engineering, Ajou University, South Korea;Department of Industrial and Information Engineering, Ajou University, South Korea;Department of Industrial and Information Engineering, Ajou University, South Korea
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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Abstract

This paper outlines an approach for applying acquiring numerical breast cancer image data and diagnosis using neural network algorithm in a way that is easy to classify between benign and malignance. This paper is an extended work related to our previous work [1]. In our previous work we used k-means algorithm to detect and diagnosis breast cancer tumor’s region. However, to find the better results from the algorithm we need to add more numerical parameters of the breast cancer image and this algorithm has limited usage when applied with more number of parameters. Even if the cancer tumor is abnormal it was quite difficult to distinguish among those tumors. This paper summarizes the different comparative study of neural network algorithms to get the best classification of the breast cancer and explains how to acquire more numerical parameters from the breast cancer image data, so that it can help doctors to diagnosis efficiently between benign and malignance tumors.