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COMPUTER AIDED DIAGNOSIS USING GMDH-TYPE NEURAL NETWORKS

使用 GMDH 型神经网络的计算机辅助诊断

基本信息

批准号：
14550401
负责人：
KONDO Tadashi
金额：
$ 0.51万
依托单位：
The University of Tokushima
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2003
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-14550401/
关键词：
GMDH Neural Networks Computer aided diagnosis ニューラルネットワークス医用画像診断

项目摘要

In this study, we developed some GMDH (Group Method of Data Handling)-type neural network algorithms which can automatically organize the optimum neural network architectures fitting the complexity of the various medical images such as MRI images, X-ray CT images, digital mammography, echo images and digital X-ray images and we applied these algorithms to the computer aided diagnosis (CAD). The GMDH-type neural network algorithms developed by us have an ability of self-organizing the optimum neural network architectures using the various neuron architectures such as sigmoid function type neuron, radial basis function type neuron and polynomial type neuron so as to fit the complexity of the various medical images. Furthermore, these algorithms have another ability of self-selecting the optimum input variables from many image characteristics so as to minimize the prediction error criterions defined as AIC (Akaike's Information Criterion) and PSS (Prediction Sum of Squares). Therefore, we can apply these GMDH-type neural network algorithms to the computer aided diagnosis (CAD) and the medical image recognition very easily.In this study, we applied these GMDH-type neural network algorithms to the various medical images such as the MIRI image of the brain, X-ray image of the stomach and X-ray CT image of the lungs and we organized the optimum neural network architectures fitting the complexity of these medical images m the computer. By using these organized neural networks in the computer, the outlines of the interested regions (ROI) of these images ware automatically extracted with the good accuracy. Furthermore, these GMDH-type neural network algorithms are applied to the computer aided diagnosis of breast cancer.

在本研究中，我们开发了一些GMDH （Group Method of Data Handling）型神经网络算法，该算法可以自动组织适合各种医学图像（如MRI图像、x射线CT图像、数字乳房x线摄影、回声图像和数字x射线图像）复杂性的最佳神经网络架构，并将这些算法应用于计算机辅助诊断（CAD）。我们开发的gmdh型神经网络算法具有利用s型函数型神经元、径向基函数型神经元和多项式型神经元等多种神经元结构自组织最优神经网络结构的能力，以适应各种医学图像的复杂性。此外，这些算法还具有从许多图像特征中自选择最优输入变量的能力，从而最小化定义为赤池信息准则（AIC）和预测平方和（PSS）的预测误差准则。因此，我们可以很容易地将这些gmdh型神经网络算法应用到计算机辅助诊断（CAD）和医学图像识别中。在本研究中，我们将这些gmdh型神经网络算法应用于各种医学图像，如大脑的MIRI图像，胃的x射线图像和肺部的x射线CT图像，并在计算机上组织适合这些医学图像复杂性的最佳神经网络架构。通过在计算机中使用这些有组织的神经网络，可以自动提取这些图像的感兴趣区域（ROI）的轮廓，并具有良好的精度。并将这些gmdh型神经网络算法应用于乳腺癌的计算机辅助诊断。