Application of artificial neural networks in ovarian cancer early detection

The artificial neural network (ANN) classifier for discriminating malignant from benign pelvic masses was constructed based on the multilayer perceptron structure. To compensate for the small training sample size and noisy data as often occurred in medical applications, special sample selection criteria are applied to improve data quality. Preprocessing steps based on biological knowledge and data mining techniques are also taken to reduce the complexity of ANN training. The original data set was divided into two sets, one for ANN training set and the other for independent validation. Two additional independent data sets were also used for the evaluation of the system.