Use of a multi-layer perceptron to predict malignancy in ovarian tumors

Advances in Neural Information Processing Systems

Volumn , Issue , 1998, Pages 978-984

  Verrelst, Herman ^a   Moreau, Yves ^a   Vandewalle, Joos ^a   Timmerman, Dirk ^b  

^a Katholieke Universiteit Leuven   (Belgium)

^b UNIVERSITY HOSPITALS LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

NEURAL NETWORKS;

CLASSIFICATION ERRORS; COLOR DOPPLER IMAGING; MULTI LAYER PERCEPTRON; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; OBJECTIVE ASSESSMENT; RECEIVER OPERATING CHARACTERISTIC CURVES; SENSITIVITY AND SPECIFICITY; SERUM TUMOR MARKERS;

DOPPLER EFFECT;

EID: 1942418003     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. Bast R. C. Jr., Klug T.L., St. John E., et al, "A radioimmunoassay using a monoclonal antibody to monitor the course of epithelial ovarian cancer, " N. Engl. J. Med., Vol. 309, pp. 883-888, 1983.
2. Timmerman D., Bourne T., Tailor A., Van Assche F.A., Vergote I., "Preoperative differentiation between benign and malignant adnexal masses, " submitted.
3. Tailor A., Jurkovic D., Bourne T.H., Collins W.P., Campbell S., "Sonographic prediction of malignancy in adnexal masses using multivariate logistic regression analysis, " Ultrasound Obstet. Gynaecol, in press, 1997.
4. Jacobs I., Oram D., Fairbanks J., et al., "A risk of malignancy index incorporating CA 125, ultrasound and menopausal status for the accurate preoperative diagnosis of ovarian cancer, " Br. J. Obstet. Gynaecol., Vol. 97, pp. 922-929, 1990.
5. Hanley J.A., McNeil B., "A method of comparing the areas under the receiver operating characteristics curves derived from the same cases, " Radiology, Vol. 148, pp. 839-843, 1983.
6. Swets J.A., "Measuring the accuracy of diagnostic systems, " Science, Vol. 240, pp. 1285-1293, 1988.
7. Galen R.S., Gambino S., Beyond normality: The predictive value and efficiency of medical diagnosis, John Wiley, New York, 1975.
8. Gill P., Murray W., Wright M., Practical Optimization, Acad. Press, New York, 1981.
9. Fletcher R., Practical methods of optimization, 2nd ed., John Wiley, New York, 1987.
10. Kirkpatrick S., Gelatt C.D., Yecchi M., "Optimization by simulated annealing, " Sci-ence, Vol. 220, pp. 621-680, 1983.
11. Rumelhart D.E., Hinton G.E., Williams R.J., "Learning representations by back- propagating errors, " Nature, Vol. 323, pp. 533-536, 1986.
12. Bishop C., Artificial Neural Networks for Pattern Recognition, OUP, Oxford, 1996.