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Cancer Diagnosis Using Artificial Neural Networks


M. Baradaran Nia, Sh. Shogian, M. H. Zarifi


Vol. 8  No. 7  pp. 233-236


Artificial Neural Networks are used to analyze electrical impedance spectroscopy (EIS) data taken from patients bladders. Using this system, malignant areas from non-malignant areas in the urinary bladder of the patient can be separated very rapidly. Four different structures of artificial neural networks (ANNs) are used. The results show that both LVQ4b and SFAM networks show minimum error rate equal to 9.52% in test phase. Also using the MLP network make it possible to have no-prediction state which makes it easy to deal with critical practical applications.


Artificial neural networks, electrical impedance spectroscopy, bladder cancer diagnosis