Machine learning techniques for classification of breast tissue

This paper presents an automated classification of breast tissue using two machine learning techniques: Feedforward neural network using the backpropagation learning algorithm (BPNN) and radial basis function network (RBFN). The two neural network models are implored basically to identify the best model for breast tissue classification after an intense comparison of experimental results. An electrical impedance spectroscopy method was used for data acquisition while BPNN and RBFN were the models implored for the execution of the classification task. The approach implored in this paper is made out of the following steps; feature extraction, feature selection and classification steps. The features are obtained using the electrical impedance spectroscopy (EIS) at the feature extraction stage. These extracted features are impedance at zero frequency (I0), the high frequency slope of phase angle, the phase angle at 500KHz, the area under spectrum, the maximum of spectrum, the normalized area, the impedance distance between spectral ends, the distance between the impedivity at I0 and the real part of the maximum frequency point and the length of the spectral curve. Information theoretic criterion is the strategy used in the proposed algorithm for feature selection and classification phase that was executed using the BPNN and RBFN. The performance measure of the two algorithms is the accuracy of the BPNN and RBFN models. The RBFN outperforms the BPNN in terms of accuracy in classifying breast tissues, minimum square error reached, and time to learn as demonstrated in the experimental results.