CT image classification based on convolutional neural network

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...

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Vydané v:Neural computing & applications Ročník 33; číslo 14; s. 8191 - 8200
Hlavní autori: Zhang, Yuezhong, Wang, Shi, Zhao, Honghua, Guo, Zhenhua, Sun, Dianmin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.07.2021
Springer Nature B.V
Predmet:
Accuracy
Algorithms
Artificial Intelligence
Artificial neural networks
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computed tomography
Computer Science
Data Mining and Knowledge Discovery
Image classification
Image Processing and Computer Vision
Medical imaging
Medical research
Neural networks
Optimization
Optimization algorithms
Physicians
Probability and Statistics in Computer Science
S. I : Intelligent Computing Methodologies in Machine learning for IoT Applications
Special Issue on Intelligent Computing Methodologies in Machine learning for IoT Applications
CT image classification
Convolution layer
Convolution neural network
CDBN model
ISSN:0941-0643, 1433-3058
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-04933-4