DocumentCode :
2315607
Title :
Detecting false benign in breast cancer diagnosis
Author :
Yang, Zheng Rong ; Lu, Weiping ; Yu, Dejin ; Harrison, Robert G.
Author_Institution :
Dept. of Phys., Heriot-Watt Univ., Edinburgh, UK
Volume :
3
fYear :
2000
fDate :
2000
Firstpage :
655
Abstract :
Reports a method for breast cancer diagnosis using a robust heteroscedastic probabilistic neural network. The network has the inherent property of clustering patients into several groups, each of which has a distinct significance level: e.g. the larger the significance level of a benign (malignant) group, the more typical the benign (malignant) symptoms. From this, false benign patients can be identified through investigating the probabilistic relationships between each benign group with a small significance level and malignant groups. A false benign analysis table has thus been designed based on this approach. By detecting false benign, the misclassification rate of malignant patients can be reduced to a minimum without significantly increasing the misclassification rate of benign patients. In applying this method to Wisconsin diagnostic breast cancer data, the correct classification rates are 100% for malignant and 98% for benign
Keywords :
cancer; image classification; mammography; medical image processing; neural nets; benign group; breast cancer diagnosis; false benign detection; malignant groups; misclassification rate; patients clustering; probabilistic relationships; robust heteroscedastic probabilistic neural network; significance level; Breast cancer; Cancer detection; Cost function; Gaussian distribution; Intelligent networks; Medical diagnostic imaging; Neural networks; Physics; Robustness; Telephony;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
ISSN :
1098-7576
Print_ISBN :
0-7695-0619-4
Type :
conf
DOI :
10.1109/IJCNN.2000.861398
Filename :
861398
Link To Document :
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