Deep multi-layered GMDH-type neural network using revised heuristic self-organization and its application to medical image diagnosis of liver cancer
Deep multi-layered GMDH-type neural network using revised heuristic self-organization and its application to medical image diagnosis of liver cancer
复制标题
修正启发式自组织深层多层GMDH型神经网络及其在肝癌医学图像诊断中的应用
DOI：
10.1007/s10015-017-0392-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
本研究提出了一种基于改进启发式自组织方法的深度多层群数据处理（GMDH）型神经网络算法，并将其应用于肝癌的医学图像诊断。深度gmdh型神经网络可以自动组织具有多个隐藏层的深度神经网络体系结构。自动选择隐层数、隐层神经元数和有用的输入变量等结构参数，以最小化预测误差准则，即Akaike信息准则（AIC）或预测平方和（PSS）。深度神经网络的结构采用改进的启发式自组织方法进行自动组织，这是一种进化计算方法。将该神经网络算法应用于肝癌医学图像的诊断，并与传统的三层s型函数神经网络的识别结果进行了比较。
In this study, the deep multi-layered group method of data handling (GMDH)-type neural network algorithm using revised heuristic self-organization method is proposed and applied to medical image diagnosis of liver cancer. The deep GMDH-type neural network can automatically organize the deep neural network architecture which has many hidden layers. The structural parameters such as the number of hidden layers, the number of neurons in hidden layers and useful input variables are automatically selected to minimize prediction error criterion defined as Akaike’s information criterion (AIC) or prediction sum of squares (PSS). The architecture of the deep neural network is automatically organized using the revised heuristic self-organization method which is a type of the evolutionary computation. This new neural network algorithm is applied to the medical image diagnosis of the liver cancer and the recognition results are compared with the conventional 3-layered sigmoid function neural network.