| Title: | GS1-2 Medical Image Analysis of Brain X-ray CT Images By Deep GMDH-Type Neural Network |
|---|---|
| Publication: | ICAROB2016 |
| Volume: | 21 |
| Pages: | 120-124 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2016.GS1-2 |
| 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 Group Method of Data Handling (GMDH)-type neural network is applied to the medical image analysis of brain X-ray CT image. In this algorithm, the deep neural network architectures which have many hidden layers and fit the complexity of the nonlinear systems, are automatically organized using the heuristic self-organization method so as to minimize the prediction error criterion defined as Akaike's Information Criterion (AIC) or Prediction Sum of Squares (PSS). The learning algorithm is the principal component-regression analysis and the accurate and stable predicted values are obtained. The recognition results show that the deep GMDH-type neural network algorithm is useful for the medical image analysis of brain X-ray CT images. |
| PDF File: | https://alife-robotics.co.jp/members2016/icarob/data/papers/GS/GS1-2.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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