Breast cancer diagnosis using Artificial Neural Network models | IEEE Conference Publication | IEEE Xplore

Breast cancer diagnosis using Artificial Neural Network models

Publisher: IEEE

Abstract:

Breast cancer is the second leading cause of cancer deaths worldwide and occurrs in one out of eight women. In this paper we develop a system for diagnosis, prognosis and...View more

Abstract:

Breast cancer is the second leading cause of cancer deaths worldwide and occurrs in one out of eight women. In this paper we develop a system for diagnosis, prognosis and prediction of breast cancer using Artificial Neural Network (ANN) models. This will assist the doctors in diagnosis of the disease. We implement four models of neural networks namely Back Propagation Algorithm, Radial Basis Function Networks, Learning vector Quantization and Competitive Learning Network Experimental results show that Learning Vector Quantization shows the best performance in the testing data set This is followed in order by CL, MLP and RBFN The high accuracy of the LVQ against the other models indicates its better ability for solving the classificatory problem of Breast Cancer diagnosis.
Date of Conference: 23-25 June 2010
Date Added to IEEE Xplore: 03 August 2010
ISBN Information:
Publisher: IEEE
Conference Location: Chengdu, China

I. Introduction

Cancer is a general term that refers to cells that grow larger than 2mm in every 3 months and multiply out of control and spreads to other parts of the body. We develop a Artificial Neural Network (ANN) models for breast cancer will be use for all type of the diagnosis and prognosis [1] [2] The American Cancer Society has predicted that about 192,370 women in the United States will be diagnosed with invasive breast cancer and 40,170 women will die in 2009 [3]. Artificial Neural network systems are made to learn this data by the use of training algorithms that may be specific to the system. Learning involves the extraction of rules or patterns from the historic data.

References

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