Electrical Impedance Spectroscopy (EIS) has been demonstrated as a powerful tool in the
diagnosis of many different medical conditions. Using the interpretation of the information
acquired by EIS it is possible to identify changes in the structure of biological tissue.
Interpretation of EIS data is usually done by linear regression or mathematical modelling of
the impedance curve. In this paper, a new method of classification of biological materials
is developed using an Impedance Spectroscope and Deep Neural Networks for the
interpretation of data. Results show that classification with neural networks is faster and more reliable than the usual approaches.
electrical impedance spectroscopy, neural networks, deep learning, biological tissue, frequency