Medical Image Recognition of Heart Regions by Deep Multi-Layered GMDH-Type Neural Network Using Principal Component-Regression Analysis

DOI

10.2991/jrnal.2015.2.3.7How to use a DOI?

Keywords

Deep neural networks, GMDH, Medical image recognition, Evolutionary computation

Abstract

In this study, a deep Group Method of Data Handling (GMDH)-type neural network using principal component-regression is applied to the medical image recognition of the heart regions. The deep GMDH-type neural network algorithm can organize the neural network architecture with many hidden layers fitting the complexity of the nonlinear systems so as to minimize the prediction error criterion defined as AIC (Akaike’s Information Criterion) or PSS (Prediction Sum of Squares). This algorithm is applied to the medical image recognition of the heart regions and it is shown that this algorithm is useful for the medical image recognition of the heart regions because deep neural network architecture is automatically organized using the principal component-regression analysis from the medical images of the heart regions.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Tadashi Kondo
AU  - Junji Ueno
AU  - Shoichiro Takao
PY  - 2015
DA  - 2015/12/01
TI  - Medical Image Recognition of Heart Regions by Deep Multi-Layered GMDH-Type Neural Network Using Principal Component-Regression Analysis
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 166
EP  - 172
VL  - 2
IS  - 3
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2015.2.3.7
DO  - 10.2991/jrnal.2015.2.3.7
ID  - Kondo2015
ER  -