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.