CT image classification based on convolutional neural network

doi:10.1007/s00521-020-04933-4

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	CT image classification based on convolutional neural network
	Zhang, Yuezhong 1; Wang, Shi 2; Zhao, Honghua 3; Guo, Zhenhua 4; Sun, Dianmin 5,6
刊名	NEURAL COMPUTING & APPLICATIONS
	2020-04-29
页码	10
关键词	Convolution neural network Convolution layer CT image classification CDBN model
ISSN号	0941-0643
DOI	10.1007/s00521-020-04933-4
英文摘要	With the rapid development of the Internet, image information is explosively growing. Traditional image classification methods are difficult to deal with huge image data and cannot meet people's requirements on the accuracy and speed of image classification. In recent years, the convolutional neural network (CNN) has been developing rapidly, and it has performed extremely well. The image classification method based on CNN breaks through the bottleneck of traditional image classification methods and becomes the mainstream image classification algorithm at present. CT image classification algorithm is one of the research hot spots in the field of medical image. The purpose of this paper is to apply convolutional neural network to CT image classification, so as to speed up CT image classification and improve the accuracy of CT image classification and so as to reduce the workload of doctors and improve work efficiency. In this paper, CT images are classified by CDBN model. Vector machine SVM is used as the feature classifier of CDBN model to enhance feature transfer and reuse so as to enrich the features. It also suppresses features that are not very useful for current tasks and improves the performance of the model. Using CDBN to classify CT images, several commonly used gray images are compared. Comparing the results of the ordinary gradient algorithm with Adam algorithm, we can get the CDBN model using Adam optimization algorithm. In CT image classification, both accuracy and speed have a good effect. The experimental results show that the training speed of CDBN model of Adam optimization algorithm in CT image classification is 3% faster than that of general gradient algorithm.
资助项目	Key Research and Development Program of Shandong Province[2018GSF118221]
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER LONDON LTD
WOS记录号	WOS:000529851100005
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/15439]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Sun, Dianmin
作者单位	1.Shandong First Med Univ, Shandong Prov Hosp, Dept Ultrasound, Jinan 250117, Shandong, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Jinan Univ, Sch Mech Engn, Jinan 250022, Shandong, Peoples R China 4.Inspur Elect Informat Ind Co Ltd, State Key Lab High End & Storage Technol, Jinan 250013, Shandong, Peoples R China 5.Shandong First Med Univ, Shandong Canc Hosp & Inst, Dept Thorac Surg, Jinan 250117, Shandong, Peoples R China 6.Shandong Acad Med Sci, Jinan 250117, Shandong, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Yuezhong,Wang, Shi,Zhao, Honghua,et al. CT image classification based on convolutional neural network[J]. NEURAL COMPUTING & APPLICATIONS,2020:10.
APA	Zhang, Yuezhong,Wang, Shi,Zhao, Honghua,Guo, Zhenhua,&Sun, Dianmin.(2020).CT image classification based on convolutional neural network.NEURAL COMPUTING & APPLICATIONS,10.
MLA	Zhang, Yuezhong,et al."CT image classification based on convolutional neural network".NEURAL COMPUTING & APPLICATIONS (2020):10.