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Deep feedback GMDH-type neural network and its application to medical image analysis of MRI brain images

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Abstract

The deep feedback group method of data handling (GMDH)-type neural network is applied to the medical image analysis of MRI brain images. In this algorithm, the complexity of the neural network is increased gradually using the feedback loop calculations. The deep 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 analysis of MRI brain images, because the optimum neural network architectures fitting the complexity of the medical images are automatically organized so as to minimize the prediction error criterion defined as AIC or PSS.

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Acknowledgements

This work was supported by (JSPS) KAKENHI 26420421.

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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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Corresponding author

Correspondence to Tadashi Kondo.

Additional information

This work was presented in part at the 21st International Symposium on Artificial Life and Robotics, Beppu, Oita, January 20-22, 2016.

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Takao, S., Kondo, S., Ueno, J. et al. Deep feedback GMDH-type neural network and its application to medical image analysis of MRI brain images. Artif Life Robotics 23, 161–172 (2018). https://doi.org/10.1007/s10015-017-0410-1

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  • DOI: https://doi.org/10.1007/s10015-017-0410-1

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