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Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification
Joint Authors
Mao, Keming
Tang, Renjie
Wang, Xinqi
Zhang, Weiyi
Wu, Haoxiang
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-05-07
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
This paper focuses on the problem of lung nodule image classification, which plays a key role in lung cancer early diagnosis.
In this work, we propose a novel model for lung nodule image feature representation that incorporates both local and global characters.
First, lung nodule images are divided into local patches with Superpixel.
Then these patches are transformed into fixed-length local feature vectors using unsupervised deep autoencoder (DAE).
The visual vocabulary is constructed based on the local features and bag of visual words (BOVW) is used to describe the global feature representation of lung nodule image.
Finally, softmax algorithm is employed for lung nodule type classification, which can assemble the whole training process as an end-to-end mode.
Comprehensive evaluations are conducted on the widely used public available ELCAP lung image database.
Experimental results with regard to different parameter setting, data augmentation, model sparsity, classifier algorithms, and model ensemble validate the effectiveness of our proposed approach.
American Psychological Association (APA)
Mao, Keming& Tang, Renjie& Wang, Xinqi& Zhang, Weiyi& Wu, Haoxiang. 2018. Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification. Complexity،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1133508
Modern Language Association (MLA)
Mao, Keming…[et al.]. Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification. Complexity No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1133508
American Medical Association (AMA)
Mao, Keming& Tang, Renjie& Wang, Xinqi& Zhang, Weiyi& Wu, Haoxiang. Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification. Complexity. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1133508
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1133508