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12 May 1995 Adaptive-sized neural-networks-based computer-aided diagnosis of microcalcifications
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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.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Akira Hasegawa, Chris Yuzheng Wu, Matthew T. Freedman M.D., and Seong Ki Mun "Adaptive-sized neural-networks-based computer-aided diagnosis of microcalcifications", Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995);

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