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

Kondo, Tadashi; Ueno, Junji; Takao, Schoichiro

doi:10.1109/candar.2013.29

Cited by 3 publications

(2 citation statements)

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“…The heuristic self-organization method is a type of the evolutional computation. In this study, the hybrid feedback GMDH-type neural network using principal component-regression analysis [1], [2] is applied to the medical image recognition of heart regions. In this feedback GMDH-type neural network algorithm, the structural parameters such as the number of feedback loops, the number of neurons in the hidden layers and the relevant input variables are automatically selected so as to minimize the prediction error criterion defined as Akaike's Information Criterion (AIC) [7] or Prediction Sum of Squares (PSS) [8].…”

Section: Introductionmentioning

confidence: 99%

“…The GMDH-type neural network algorithms were proposed in our early works [1]- [4] and the GMDH-type neural networks were applied to the medical image diagnosis of lung cancer [1], liver cancer [2] and the medical image analysis of the heart [3]. The GMDH-type neural network can automatically organize the neural network architectures by using heuristic selforganization method [5], [6] which is the basic premise of the GMDH algorithm [5], [6], [8].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid feedback GMDH-type neural network using principal component-regression analysis and its application to medical image recognition of heart regions

Kondo

Ueno

Takao

2014

2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Ad

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Hybrid feedback Group Method of Data Handling(GMDH)-type neural network using principal componentregression analysis is applied to the medical image recognition of the heart regions. In the GMDH-type neural network, the multilayered deep neural networks are automatically organized so as to fit the complexity of the nonlinear system and, in general, the architectures of the GMDH-type neural network have many hidden layers and become complex for the nonlinear systems. In the multi-layered deep GMDH-type neural network with many hidden layers, the multi-colinearity occurs and the perdition accuracy become worse and the prediction values become unstable. In the GMDH-type neural network used in this study, the principal component-regression analysis is used as the learning algorithm of the neural network and the multicolinearity do not occur and accurate and stable GMDH-type neural network architectures are automatically organized so as to fit the complexity of the nonlinear system. Furthermore, in this algorithm, three types of neural networks, such as sigmoid function neural network, radial basis function (RBF) neural network and polynomial neural network, can be generated using three types of neuron architectures, and the neural network architecture which fits the complexity of medical images, is selected from these three neural network architectures. This GMDH-type neural network is applied to the medical image recognition of the heart regions and it is shown that this GMDHtype neural network is useful for the medical image recognition of the heart regions.

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