Development of 3 dimensional medical image diagnosis system using artificial intelligence
| Project/Area Number |
22560403
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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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 |
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) |
2010 – 2012
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| Project Status |
Completed (Fiscal Year 2012)
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| Budget Amount *help |
¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2012: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2011: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2010: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
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| Keywords | システム情報(知識)処理 / 人工ニューラルネットワーク / ＧＭＤＨ / コンピュータ支援診断 / GMDH / マルチスライスCT / 知識工学 |
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| Research Abstract |
In this study, three dimensional medical image diagnosissystem using artificial intelligence was developed. The artificial intelligence developed in this study, are the revised GMDH-type neural network algorithms and these algorithms can automatically organize the optimum artificial neural network architectures fitting the complexity of the medical images using X-ray CT images. In the conventional GMDH-type neural network algorithms, multi-colinearity occurred and prediction values become unstable. In the revised GMDH-type neural network algorithms developed in this study, the principal component-regression analysis is used and multi-colinearity does not occur and accurate prediction values are obtained. The revised GMDH-typeneural networks ware applied to medical image diagnosis of the lung cancer and the results were compared with the conventional sigmoid function neural network trained using the back propagation method and it was shown that these algorithms were useful for medical image diagnosisof the lung cancer.
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Report
(4 results)
Research Products
(54 results)
