2018年6月1日
Deep feedback GMDH-type neural network and its application to medical image analysis of MRI brain images
Artificial Life and Robotics
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- 巻
- 23
- 号
- 2
- 開始ページ
- 161
- 終了ページ
- 172
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1007/s10015-017-0410-1
- 出版者・発行元
- Springer Tokyo
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.
- ID情報
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- DOI : 10.1007/s10015-017-0410-1
- ISSN : 1614-7456
- ISSN : 1433-5298
- SCOPUS ID : 85035758223