Medical image diagnosis of liver cancer by feedback GMDH-type neural network using knowledge base

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• 作者：Tadashi Kondo (1)
  Junji Ueno (1)
  Shoichiro Takao (1)
  
• 关键词：Neural networks ; GMDH ; Medical image diagnosis ; Artificial intelligence
• 刊名：Artificial Life and Robotics
• 出版年：2013
• 出版时间：February 2013
• 年：2013
• 卷：17
• 期：3-4
• 页码：488-494
• 全文大小：394KB
• 参考文献：1. Kondo T, Ueno J (2007) Logistic GMDH-type neural network and its application to identification of X-ray film characteristic curve. J Adv Comput Intell Intell Inf 11(3):312鈥?18
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  3. Farlow SJ (ed) (1984) Self-organizing methods in modeling, GMDH-type algorithm. Marcel Dekker, Inc., New York
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  5. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control AC-19(6):716鈥?23 CrossRef
  6. Tamura H, Kondo T (1980) Heuristics free group method of data handling algorithm of generating optimum partial polynomials with application to air pollution prediction. Int J Syst Sci 11(9):1095鈥?111 CrossRef
  7. Draper NR, Smith H (1981) Applied regression analysis. Wiley, New York
  
• 作者单位：Tadashi Kondo (1)
  Junji Ueno (1)
  Shoichiro Takao (1)
  
  1. Graduate School of Health Sciences, The University of Tokushima, 3-18-15 Kuramoto-cho, Tokushima, 770-8509, Japan
  
• ISSN：1614-7456

文摘

A revised group method of data handling (GMDH)-type neural network algorithm using knowledge base for medical image diagnosis, is proposed and applied to medical image diagnosis of liver cancer. In this algorithm, the knowledge base for medical image diagnosis is used for organizing the neural network architecture for medical image diagnosis. Furthermore, the revised GMDH-type neural network algorithm has a feedback loop and can identify the characteristics of the medical images accurately using feedback loop calculations. It is shown that the revised GMDH-type neural network is accurate and a useful method for the medical image diagnosis of the liver cancer.