Adaptive-sized neural-networks-based computer-aided diagnosis of microcalcifications

Akira Hasegawa; Chris Yuzheng Wu; Matthew T. Freedman M.D.; Seong Ki Mun

doi:10.1117/12.208727

12 May 1995 Adaptive-sized neural-networks-based computer-aided diagnosis of microcalcifications

Akira Hasegawa, Chris Yuzheng Wu, Matthew T. Freedman M.D., Seong Ki Mun

Proceedings Volume 2434, Medical Imaging 1995: Image Processing; (1995) https://doi.org/10.1117/12.208727
Event: Medical Imaging 1995, 1995, San Diego, CA, United States

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.

Citation Download Citation

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); https://doi.org/10.1117/12.208727

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