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Proceedings Paper

Adaptive-sized neural-networks-based computer-aided diagnosis of microcalcifications
Author(s): Akira Hasegawa; Chris Yuzheng Wu; Matthew T. Freedman; Seong Ki Mun
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Paper Abstract

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.

Paper Details

Date Published: 12 May 1995
PDF: 6 pages
Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); doi: 10.1117/12.208727
Show Author Affiliations
Akira Hasegawa, Georgetown Univ. Medical Ctr. (United States)
Chris Yuzheng Wu, Georgetown Univ. Medical Ctr. (United States)
Matthew T. Freedman, Georgetown Univ. Medical Ctr. (United States)
Seong Ki Mun, Georgetown Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 2434:
Medical Imaging 1995: Image Processing
Murray H. Loew, Editor(s)

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