gmdh型神经网络及其在肺部医学图像识别中的应用

T. Kondo, A. Pandya, J. Zurada
{"title":"gmdh型神经网络及其在肺部医学图像识别中的应用","authors":"T. Kondo, A. Pandya, J. Zurada","doi":"10.1109/SICE.1999.788720","DOIUrl":null,"url":null,"abstract":"The GMDH (group method of data handling)-type neural networks and their application to the medical image recognition of the lungs are described. The GMDH-type neural networks have both characteristics of the GMDH and the conventional multilayered neural network and can automatically organize the optimum neural network architecture by using the heuristic self-organization method. In the GMDH-type neural networks, many types of neurons can be used to organize neural networks' architecture and neurons' characteristics which fit the complexity of the nonlinear system. They are automatically selected by using the error criterion defined as AIC (Akaike's information criterion). Therefore, many types of nonlinear systems can be automatically modeled by using the GMDH-type neural networks. In the paper, the GMDH-type neural networks are applied to the medical image recognition of the lungs.","PeriodicalId":103164,"journal":{"name":"SICE '99. Proceedings of the 38th SICE Annual Conference. International Session Papers (IEEE Cat. No.99TH8456)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"GMDH-type neural networks and their application to the medical image recognition of the lungs\",\"authors\":\"T. Kondo, A. Pandya, J. Zurada\",\"doi\":\"10.1109/SICE.1999.788720\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The GMDH (group method of data handling)-type neural networks and their application to the medical image recognition of the lungs are described. The GMDH-type neural networks have both characteristics of the GMDH and the conventional multilayered neural network and can automatically organize the optimum neural network architecture by using the heuristic self-organization method. In the GMDH-type neural networks, many types of neurons can be used to organize neural networks' architecture and neurons' characteristics which fit the complexity of the nonlinear system. They are automatically selected by using the error criterion defined as AIC (Akaike's information criterion). Therefore, many types of nonlinear systems can be automatically modeled by using the GMDH-type neural networks. In the paper, the GMDH-type neural networks are applied to the medical image recognition of the lungs.\",\"PeriodicalId\":103164,\"journal\":{\"name\":\"SICE '99. Proceedings of the 38th SICE Annual Conference. International Session Papers (IEEE Cat. No.99TH8456)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SICE '99. Proceedings of the 38th SICE Annual Conference. International Session Papers (IEEE Cat. No.99TH8456)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SICE.1999.788720\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SICE '99. Proceedings of the 38th SICE Annual Conference. International Session Papers (IEEE Cat. No.99TH8456)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SICE.1999.788720","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 26

摘要

介绍了GMDH型神经网络及其在肺部医学图像识别中的应用。GMDH型神经网络同时具有GMDH和传统多层神经网络的特点,采用启发式自组织方法自动组织出最优的神经网络结构。在gmdh型神经网络中,可以使用多种类型的神经元来组织神经网络的结构和神经元的特性,以适应非线性系统的复杂性。使用定义为AIC(赤池信息准则)的错误准则自动选择它们。因此,利用gmdh型神经网络可以对许多类型的非线性系统进行自动建模。本文将gmdh型神经网络应用于肺部医学图像识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
GMDH-type neural networks and their application to the medical image recognition of the lungs
The GMDH (group method of data handling)-type neural networks and their application to the medical image recognition of the lungs are described. The GMDH-type neural networks have both characteristics of the GMDH and the conventional multilayered neural network and can automatically organize the optimum neural network architecture by using the heuristic self-organization method. In the GMDH-type neural networks, many types of neurons can be used to organize neural networks' architecture and neurons' characteristics which fit the complexity of the nonlinear system. They are automatically selected by using the error criterion defined as AIC (Akaike's information criterion). Therefore, many types of nonlinear systems can be automatically modeled by using the GMDH-type neural networks. In the paper, the GMDH-type neural networks are applied to the medical image recognition of the lungs.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Model reference feedback and PID control for interval plants A compiler design for IEC 1131-3 standard languages of programmable logic controllers Mass measurement based on the law of conservation of momentum Generation of robot control circuit using EHW approach Theoretical study and experiment on frequency characteristics of a probe microphone
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1