Use of a Multi-Layer Perceptron to Predict Malignancy in Ovarian Tumors

We discuss the development of a Multi-Layer Perceptron neural network classifier for use in preoperative differentiation between benign and malignant ovarian tumors. As the Mean Squared classification Error is not sufficient to make correct and objective assessments about the performance of the neural classifier, the concepts of sensitivity and specificity are introduced and combined in Receiver Operating Characteristic curves. Based on objective observations such as sonomorphologic criteria, color Doppler imaging and results from serum tumor markers, the neural network is able to make reliable predictions with a discriminating performance comparable to that of experienced gynecologists.

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