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Deep multi-layered GMDH-type neural network using revised heuristic self-organization and its application to medical image diagnosis of liver cancer

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Abstract

In this study, the deep multi-layered group method of data handling (GMDH)-type neural network algorithm using revised heuristic self-organization method is proposed and applied to medical image diagnosis of liver cancer. The deep GMDH-type neural network can automatically organize the deep neural network architecture which has many hidden layers. The structural parameters such as the number of hidden layers, the number of neurons in hidden layers and useful input variables are automatically selected to minimize prediction error criterion defined as Akaike’s information criterion (AIC) or prediction sum of squares (PSS). The architecture of the deep neural network is automatically organized using the revised heuristic self-organization method which is a type of the evolutionary computation. This new neural network algorithm is applied to the medical image diagnosis of the liver cancer and the recognition results are compared with the conventional 3-layered sigmoid function neural network.

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Acknowledgements

This work was supported by JSPS KAKENHI Grant Number JP15K06145.

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Authors and Affiliations

  1. Graduate School of Health Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8509, Japan

    Shoichiro Takao, Junji Ueno & Tadashi Kondo

  2. Tokushima Medical Informatics Laboratory, 264-5 Otubo Hachiman-cho, Tokushima, 770-8079, Japan

    Sayaka Kondo

Authors
  1. Shoichiro Takao
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  2. Sayaka Kondo
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  3. Junji Ueno
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  4. Tadashi Kondo
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Correspondence to Tadashi Kondo.

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Takao, S., Kondo, S., Ueno, J. et al. Deep multi-layered GMDH-type neural network using revised heuristic self-organization and its application to medical image diagnosis of liver cancer. Artif Life Robotics 23, 48–59 (2018). https://doi.org/10.1007/s10015-017-0392-z

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  • DOI: https://doi.org/10.1007/s10015-017-0392-z

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