Authors
Tadashi Kondo, Junji Ueno, Shoichiro Takao
Corresponding Author
Tadashi Kondo
Available Online 1 March 2016.
DOI
https://doi.org/10.2991/jrnal.2016.2.4.11
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 ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).