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【答案】应助回帖
★ ★ ★ ★ ★ ★ ★ ★ ★ ★
感谢参与，应助指数 +1
pxm_neu: 金币+10, 谢谢了 2016-01-12 20:08:51
心静_依然: LS-EPI+1, 感谢应助 2016-01-13 09:44:33
Emphysema Classification Using Convolutional Neural Networks
作者 ei, XM (Pei, Xiaomin)
编者:Liu, H; Kubota, N; Zhu, X; Dillmann, R; Zhou, D
INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2015, PT I
丛书: Lecture Notes in Artificial Intelligence
卷: 9244
页: 455-461
DOI: 10.1007/978-3-319-22879-2_42
出版年: 2015
查看期刊信息
会议名称
会议: 8th International Conference on Intelligent Robotics and Applications (ICIRA)
会议地点: Portsmouth, ENGLAND
会议日期: AUG 24-27, 2015
摘要
There has been paid more and more attention in diagnosing emphysema using High-resolution Computed Tomography. This may lead to improve both understanding and computer-aided diagnosis. We propose a novel classification framework using convolutional neural network(CNN). This model automatically extracts features from the raw image and generates classification. Experiments have been conducted on the database from clinical. Results a recognition rate of 92.54% for classification two kinds of emphysema with normal. The designed convolutional neural networks can get better results for classifying one kind of emphysema with normal.
关键词
作者关键词:High-resolution computed tomography; Emphysema; Convolutional neural network
KeyWords Plus:COMPUTED-TOMOGRAPHY; PULMONARY-EMPHYSEMA; QUANTIFICATION; IMAGES; COPD
作者信息
通讯作者地址: Pei, XM (通讯作者)
[显示增强组织信息的名称] Liaoning Shihua Univ, Coll Informat & Control Engn, Fushun, Peoples R China.
地址:
[显示增强组织信息的名称] [ 1 ] Liaoning Shihua Univ, Coll Informat & Control Engn, Fushun, Peoples R China
电子邮件地址:pxm_neu@126.com
出版商
SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
类别 / 分类
研究方向:Automation & Control Systems; Computer Science; Robotics
Web of Science 类别:Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Robotics
文献信息
文献类型roceedings Paper
语种:English
入藏号: WOS:000364714000042
ISBN:978-3-319-22879-2; 978-3-319-22878-5
ISSN: 0302-9743
其他信息
IDS 号: BD9IC
Web of Science 核心合集中的 "引用的参考文献": 23
Web of Science 核心合集中的 "被引频次": 0 |
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