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Medical image diagnosis of kidney regions by deep feedback GMDH-type neural network using principal component-regression analysis

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Abstract

The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image recognition of kidney regions. In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network, and the accurate and stable predicted values are obtained. The neural network architecture is automatically organized so as to fit the complexity of the medical images using the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The recognition results show that the deep feedback GMDH-type neural network algorithm is useful for the medical image recognition of kidney regions, because the optimum neural network architecture is automatically organized.

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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

    Tadashi Kondo, Junji Ueno & Shoichiro Takao

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

    Sayaka Kondo

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

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Kondo, T., Kondo, S., Ueno, J. et al. Medical image diagnosis of kidney regions by deep feedback GMDH-type neural network using principal component-regression analysis. Artif Life Robotics 22, 1–9 (2017). https://doi.org/10.1007/s10015-016-0337-y

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  • DOI: https://doi.org/10.1007/s10015-016-0337-y

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