ID | 112961 |
Author |
Kondo, Tadashi
Tokushima University
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Ueno, Junji
Tokushima University
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Keywords | Deep neural networks
GMDH
Medical image diagnosis
Evolutionary computation
lung cancer
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Content Type |
Journal Article
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Description | The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image diagnosis of lung cancer. The deep feedback GMDH-type neural network can identified very complex nonlinear systems using heuristic self-organization method which is a type of evolutionary computation. The deep neural network architectures are organized so as to minimize the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network. It is shown that the deep feedback GMDH-type neural network algorithm is useful for the medical image diagnosis of lung cancer because deep neural network architectures are automatically organized using only input and output data.
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Journal Title |
Journal of Robotics, Networking and Artificial Life
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ISSN | 23526386
24059021
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Publisher | Atlantis Press
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Volume | 2
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Issue | 4
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Start Page | 252
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End Page | 257
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Published Date | 2016-02-29
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Rights | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/
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language |
eng
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departments |
Medical Sciences
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