Adaptive-sized neural-networks-based computer-aided diagnosis of microcalcifications
Akira Hasegawa, Chris Y. Wu, Matthew T. Freedman, Seong K. Mun · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
In this report, we present an adaptive-sized neural network model for the detection of microcalcifications. The neural network has capabilities of automatically adjusting the network size depending on the training set, of rejecting unknown inputs, and of fast learning. When the adaptive-sized neural network is used, the user can find the optimal network size without trial and error. In addition, the reliability of the network performance is high because of the rejection of unlearned inputs. The inputs for the neural network used in this study were 11 X 11 pixel sub-images that were extracted from digitized mammograms. The experiments in 83.3% sensitivity, 84.3% specificity, and 22.4% rejection rate. The weight patterns after learning process and the dependency of the network performance on the order of presenting training examples were also studied.