Detecting false benign in breast cancer diagnosis

Zheng Rong Yang, Weiping Lu, Dejin Yu, Robert G. Harrison

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)


We report a new 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 novel 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 (WDBC) data, the correct classification rates are 100% for malignant and 98% for benign.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Number of pages4
Publication statusPublished - 2000
Event2000 International Joint Conference on Neural Networks - Como, Italy
Duration: 24 Jul 200027 Jul 2000


Conference2000 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2000

Fingerprint Dive into the research topics of 'Detecting false benign in breast cancer diagnosis'. Together they form a unique fingerprint.

Cite this