| Title: | GS1-3 Medical Image Diagnosis of Lung Cancer by Deep Feedback GMDH-Type Neural Network |
|---|---|
| Publication: | ICAROB2016 |
| Volume: | 21 |
| Pages: | 125-129 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2016.GS1-3 |
| Author(s): | Tadashi Kondo, Junji Ueno, Shoichiro Takao |
| Publication Date: | January 29, 2016 |
| Keywords: | Deep neural networks, GMDH, Medical image recognition, Evolutionary computation |
| Abstract: | 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. |
| PDF File: | https://alife-robotics.co.jp/members2016/icarob/data/papers/GS/GS1-3.pdf |
| Copyright: | © 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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