Medical image analysis of abdominal X-ray CT images by deep multi-layered GMDH-type neural network
Medical image analysis of abdominal X-ray CT images by deep multi-layered GMDH-type neural network
复制标题
深层多层GMDH型神经网络对腹部X线CT图像的医学图像分析
DOI：
10.1007/s10015-017-0420-z
复制
发表时间：
2018
影响因子：
0.9
通讯作者：
Tadashi Kondo
中科院分区：
文献类型：
--
作者：
Shoichiro Takao;Sayaka Kondo;Junji Ueno;Tadashi Kondo
在这项研究中，深层多层组数据处理方法（GMDH）型神经网络应用于腹部X射线计算机断层扫描（CT）图像的医学图像分析。使用深层多层GMDH型神经网络算法自动组织具有许多隐藏层的深层神经网络架构，以便最小化被定义为赤池信息准则（AIC）或预测平方和（PSS）的预测误差准则。医学图像的特征非常复杂，因此深度神经网络架构对于医学图像诊断和医学图像识别非常有用。在这项研究中，它表明，这种深层多层GMDH型神经网络是有用的腹部X射线CT图像的医学图像分析。
In this study, a deep multi-layered group method of data handling (GMDH)-type neural network is applied to the medical image analysis of the abdominal X-ray computed tomography (CT) images. The deep neural network architecture which has many hidden layers are automatically organized using the deep multi-layered GMDH-type neural network algorithm so as to minimize the prediction error criterion defined as Akaike’s information criterion (AIC) or prediction sum of squares (PSS). The characteristics of the medical images are very complex and therefore the deep neural network architecture is very useful for the medical image diagnosis and medical image recognition. In this study, it is shown that this deep multi-layered GMDH-type neural network is useful for the medical image analysis of abdominal X-ray CT images.