Using artificial neural networks to predict malignancy of ovarian tumors

Author

Lu, C. ; De Brabanter, Jos ; Van Huffel, Sabine ; Vergote, I. ; Timmerman, D.

Author_Institution

Dept. of Electr. Eng., Katholieke Univ., Leuven, Belgium

Volume

2

fYear

2001

fDate

2001

Firstpage

1637

Abstract

This paper discusses the application of artificial neural networks (ANNs) to preoperative discrimination between benign and malignant ovarian tumors. With the input variables selected by logistic regression analysis, two types of feed-forward neural networks were built: multi-layer perceptrons (MLPs) and generalized regression networks (GRNNs). We assess the performance of the models using the Receiver Operating Characteristic (ROC) curve, particularly the area under the ROC curves (AUC), and statistically compare the cross-validated estimate of the AUC of different models.

Keywords

biological organs; cancer; feedforward neural nets; gynaecology; multilayer perceptrons; statistical analysis; tumours; area under ROC curves; artificial neural networks; benign tumors; common gynecological problem; cross-validated estimate; generalized regression networks; logistic regression; malignancy index; model performance assessment; ovarian masses; ovarian tumors malignancy prediction; receiver operating characteristic curve; Artificial neural networks; Cancer; Feedforward neural networks; Feedforward systems; Input variables; Logistics; Multi-layer neural network; Neoplasms; Neural networks; Regression analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE

ISSN

1094-687X

Print_ISBN

0-7803-7211-5

Type

conf

DOI

10.1109/IEMBS.2001.1020528

Filename

1020528