Comparing Performance of Different Neural Networks for Early Detection of Cancer from Benign Hyperplasia of Prostate

Abstract

Prostate cancer is one of the most common types of cancer found in men. Presenting a classifier in order classifies between Prostate Cancer (PCa) and benign hyperplasia of prostate (BPH), has been great challenge among computer experts and medical specialists. There are a number of techniques proposed to perform such classification. Neural networks are one of the artificial intelligent techniques that have successful examples when applying to such problems. The increasing demand of Artificial Neural Network applications for predicting the disease shows better performance in the field of medical decision-making. This paper presents a comparison of neural network techniques for classification prostate neoplasia diseases. The classification performance obtained by four different types of neural networks for comparison are Back Propagation Neural Network (BPNN), General  Regression Neural Network(GRNN), Probabilistic Neural Network (PNN) and  Radial Basis Function Neural Network (RBFNN). Result of these evaluation show that the overall performance of RBFNN can be apply successfully for detecting and diagnosing the cancer from benign hyperplasia of prostate.