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

13 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


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

Cite this