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首页> 外文会议>International Conference on Intelligent Informatics and Biomedical Sciences >The 3-Dimensional Medical Image Recognition of Right and Left Kidneys by Deep GMDH-type Neural Network
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The 3-Dimensional Medical Image Recognition of Right and Left Kidneys by Deep GMDH-type Neural Network

机译：深化Gmdh型神经网络右侧和左肾的三维医学图像识别

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In this study, the deep multi-layered Group Method of Data Handling (GMDH)-type neural network algorithm using principal component-regression analysis is applied to recognition problems of the right and left kidney regions. The deep multi-layered GMDH-type neural network algorithm can automatically organize the deep neural network architectures which have many hidden layers and these deep neural networks can identify the characteristics of very complex nonlinear systems. The architecture of the deep neural network with many hidden layers is automatically organized using the heuristic self-organization method, so as to minimize the prediction error criterion defined as Akaike's information criterion (AIC) or Prediction Sum of Squares (PSS). The heuristic self-organization method is a type of the evolutional computation. In this deep GMDH-type neural network, principal component-regression analysis is used as the learning algorithm of the weights in the deep GMDH-type neural network, and multi-colinearity does not occur and stable and accurate prediction values are obtained. This new algorithm is applied to the medical image recognitions of the right and left kidney regions. The optimum neural network architectures, which fit the complexity of the right and left kidney regions, are automatically organized and the right and left kidney regions are automatically recognized and extracted by the organized deep GMDH-type neural networks. The recognition results are compared with the conventional sigmoid function neural network trained using back propagation method and it is shown that this deep GMDH-type neural networks are useful for the medical image recognition problems of the right and left kidney regions.
机译：在该研究中，使用主成分回归分析的数据处理（GMDH）型数据网络算法的深度多层组方法应用于右肾区域的识别问题。深度多层GMDH型神经网络算法可以自动组织具有许多隐藏层的深神经网络架构，这些深度神经网络可以识别非常复杂的非线性系统的特性。使用启发式自组织方法自动组织具有许多隐藏层的深神经网络的体系结构，从而最小化定义为Akaike的信息标准（AIC）或预测方块（PSS）的预测误差标准。启发式自组织方法是一种进化计算。在这种深入的GMDH型神经网络中，主要成分回归分析用作深入的GMDH型神经网络中的权重的学习算法，并且没有发生多环性，并且获得稳定且精确的预测值。这种新算法应用于右肾区域的医学图像识别。最佳的神经网络架构适合右肾区域的复杂性，自动组织，右肾脏区域被组织的深的GMDH型神经网络自动识别和提取。将识别结果与使用反向传播方法训练的传统的S形函数神经网络进行比较，并且显示该深度GMDH型神经网络对于右肾区域的医学图像识别问题非常有用。

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