Medical Image Diagnosis of Lung Cancer by Deep Feedback GMDH-Type Neural Network

DOI

10.2991/jrnal.2016.2.4.11How to use a DOI?

Keywords

Deep neural networks, GMDH, Medical image diagnosis, Evolutionary computation, lung cancer

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.

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  - 2016
DA  - 2016/03/01
TI  - Medical Image Diagnosis of Lung Cancer by Deep Feedback GMDH-Type Neural Network
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 252
EP  - 257
VL  - 2
IS  - 4
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2016.2.4.11
DO  - 10.2991/jrnal.2016.2.4.11
ID  - Kondo2016
ER  -