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Hybrid Multi-layered GMDH-Type Neural Network Using Principal Component-Regression Analysis and Its Application to Medical Image Diagnosis of Lung Cancer

Kondo, T. ; Ueno, J. ; Takao, S.

2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom), 2012, p.20-27

IEEE

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  • Título:
    Hybrid Multi-layered GMDH-Type Neural Network Using Principal Component-Regression Analysis and Its Application to Medical Image Diagnosis of Lung Cancer
  • Autor: Kondo, T. ; Ueno, J. ; Takao, S.
  • Assuntos: CAD ; GMDH ; Medical image ; Neural network
  • É parte de: 2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom), 2012, p.20-27
  • Descrição: In this study, hybrid multi-layered Group Method of Data Handling (GMDH)-type neural network algorithm using principal component-regression analysis is proposed and applied to the computer aided image diagnosis (CAD) of lung cancer. In the GMDH-type neural network, heuristic self-organization method, which is a kind of evolutional computation, is used to organize the neural network architecture. But, multi-co linearity occurs and prediction values become unstable. In this study, hybrid multi-layered GMDH-type neural network using principal component-regression analysis is proposed. In this algorithm, multi-co linearity does not occur and accurate prediction values are obtained. This new algorithm is applied to the medical image diagnosis of lung cancer. First, the GMDH-type neural network which recognizes the lung regions, is automatically organized using multi-detector row CT (MDCT) images of the lung, and the lung regions are recognized and extracted. Then, new another GMDH-type neural network is automatically organized using the extracted image of lung, and the candidate regions of the lung cancer is recognized and extracted. The recognition results are compared with the conventional sigmoid function neural network trained using back propagation method and it is shown that this algorithm is useful for CAD of lung cancer.
  • Editor: IEEE
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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