| Project/Area Number |
24560497
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
System engineering
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| Research Institution | The University of Tokushima |
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Principal Investigator |
UENO Junji 徳島大学, ヘルスバイオサイエンス研究部, 教授 (60116788)
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| Co-Investigator(Kenkyū-buntansha) |
KONDO Tadashi 徳島大学, 大学院ヘルスバイオサイエンス研究部, 教授 (80205559)
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| Project Period (FY) |
2012-04-01 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2013: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2012: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | 人工ニューラルネットワーク / GMDH / マルチスライスCT / コンピュータ支援診断 / 医用画像診断 / 人工知能技術 |
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| Outline of Final Research Achievements |
In this study, new artificial neural network algorithms which are called as revised Group Method of Data Handling (GMDH)-type neural network algorithms, were proposed and we applied these algorithms to the medical image diagnosis of the liver cancer and the medical image recognition of abdominal multi-organs, and we developed the computer aided diagnosis (CAD) system using abdominal multi-detector row CT images. In this neural network algorithms, the principal component-regression analysis method is used to learn the weights of the neural networks and the neural network architectures are automatically organized using the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS) so as to fit the complexity of the medical images. In this study, we applied new neural network algorithms to the medical image diagnosis of liver cancer and it was shown that new neural networks were useful to the medical image diagnosis of liver cancer.
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