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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 …