NSCR-Based DenseNet for Lung Tumor Recognition Using Chest CT Image
Joint Authors
Zhou, Tao
Bingqiang, Huo
Huiling, Lu
Zaoli, Yang
Hongbin, Shi
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-16
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Nonnegative sparse representation has become a popular methodology in medical analysis and diagnosis in recent years.
In order to resolve network degradation, higher dimensionality in feature extraction, data redundancy, and other issues faced when medical images parameters are trained using convolutional neural networks.
Lung tumors in chest CT image based on nonnegative, sparse, and collaborative representation classification of DenseNet (DenseNet-NSCR) are proposed by this paper: firstly, initialization parameters of pretrained DenseNet model using transfer learning; secondly, training DenseNet using CT images to extract feature vectors for the full connectivity layer; thirdly, a nonnegative, sparse, and collaborative representation (NSCR) is used to represent the feature vector and solve the coding coefficient matrix; fourthly, the residual similarity is used for classification.
The experimental results show that the DenseNet-NSCR classification is better than the other models, and the various evaluation indexes such as specificity and sensitivity are also high, and the method has better robustness and generalization ability through comparison experiment using AlexNet, GoogleNet, and DenseNet-201 models.
American Psychological Association (APA)
Zhou, Tao& Bingqiang, Huo& Huiling, Lu& Zaoli, Yang& Hongbin, Shi. 2020. NSCR-Based DenseNet for Lung Tumor Recognition Using Chest CT Image. BioMed Research International،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1136061
Modern Language Association (MLA)
Zhou, Tao…[et al.]. NSCR-Based DenseNet for Lung Tumor Recognition Using Chest CT Image. BioMed Research International No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1136061
American Medical Association (AMA)
Zhou, Tao& Bingqiang, Huo& Huiling, Lu& Zaoli, Yang& Hongbin, Shi. NSCR-Based DenseNet for Lung Tumor Recognition Using Chest CT Image. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1136061
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136061