A neural network approach to microcalcification detection
Kevin S. Woods, Christopher C. Doss, Kevin W. Bowyer, Laurence P. Clarke, Robert Alfred Clark · 2003
A supervised dynamic neural network is used to detect microcalcifications in digitized mammograms. A segmentation process is used to extract candidate objects from the mammogram, and then the neural network is used to determine if the candidate object is a microcalcification. A simple postprocessing procedure is applied to the results to check for clusters of microcalcifications. The neural network method is compared to the K-nearest neighbor method. The artificial neural network (ANN) used for pattern classification is called cascade correlation (CC). The true positive detection rate of the CC ANN for individual microcalcifications is 73% and 92% for nonmicrocalcifications.>