Breast cancer is one of the leading causes of mortality among women, and the early diagnosis is of significant clinical importance. In this paper, neural network based classifier is proposed which used the electrical impedance data as input data set. Multi-Layer Perceptron (MLP), network is designed with systematic experimentation. In our experiments, we utilize the criteria of mean squared error and absolute classification accuracy to concretely evaluate the performances of the NN based classifier. The experimental results demonstrate that the proposed classifier works as an elegant classifier with the accuracy of 96%
Index Terms-Breast cancer, Electrical impedance, MLP
Breast cancer presents a serious medical and social problem worldwide. Early detection is a key to effective breast cancer treatment. Electrical Impedance Tomography (EIT) is a medical imaging technique that reconstructs internal electrical conductivity distribution of a body from impedance data, which can be used for detection of breast cancer. In this paper publicly available data of breast cancer is used to design and optimized Radial Basis Function (RBF) Neural Network based Classifier. Classifier is tested for generalization, which proves as an elegant classifier with the accuracy of 93%.
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