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Medical image analysis of abdominal X-ray CT images by deep multi-layered GMDH-type neural network

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Abstract

In this study, a deep multi-layered group method of data handling (GMDH)-type neural network is applied to the medical image analysis of the abdominal X-ray computed tomography (CT) images. The deep neural network architecture which has many hidden layers are automatically organized using the deep multi-layered GMDH-type neural network algorithm so as to minimize the prediction error criterion defined as Akaike’s information criterion (AIC) or prediction sum of squares (PSS). The characteristics of the medical images are very complex and therefore the deep neural network architecture is very useful for the medical image diagnosis and medical image recognition. In this study, it is shown that this deep multi-layered GMDH-type neural network is useful for the medical image analysis of abdominal X-ray CT images.

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Acknowledgements

This work was supported by (JSPS) KAKENHI 15K06145.

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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. Medical image analysis of abdominal X-ray CT images by deep multi-layered GMDH-type neural network. Artif Life Robotics 23, 271–278 (2018). https://doi.org/10.1007/s10015-017-0420-z

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  • DOI: https://doi.org/10.1007/s10015-017-0420-z

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