Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/3078374 |
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| _version_ | 1850164316428304384 |
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| author | Keming Mao Renjie Tang Xinqi Wang Weiyi Zhang Haoxiang Wu |
| author_facet | Keming Mao Renjie Tang Xinqi Wang Weiyi Zhang Haoxiang Wu |
| author_sort | Keming Mao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ea7e539b2edd40428d357217b8b0f338 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ea7e539b2edd40428d357217b8b0f3382025-08-20T02:22:01ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/30783743078374Feature Representation Using Deep Autoencoder for Lung Nodule Image ClassificationKeming Mao0Renjie Tang1Xinqi Wang2Weiyi Zhang3Haoxiang Wu4College of Software, Northeastern University, Shenyang, Liaoning Province 110004, ChinaChina Mobile Group Zhejiang Co., Ltd., Hanzhou, Zhejiang Province 310016, ChinaCollege of Software, Northeastern University, Shenyang, Liaoning Province 110004, ChinaCollege of Software, Northeastern University, Shenyang, Liaoning Province 110004, ChinaCollege of Software, Northeastern University, Shenyang, Liaoning Province 110004, ChinaThis 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.http://dx.doi.org/10.1155/2018/3078374 |
| spellingShingle | Keming Mao Renjie Tang Xinqi Wang Weiyi Zhang Haoxiang Wu Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification Complexity |
| title | Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification |
| title_full | Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification |
| title_fullStr | Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification |
| title_full_unstemmed | Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification |
| title_short | Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification |
| title_sort | feature representation using deep autoencoder for lung nodule image classification |
| url | http://dx.doi.org/10.1155/2018/3078374 |
| work_keys_str_mv | AT kemingmao featurerepresentationusingdeepautoencoderforlungnoduleimageclassification AT renjietang featurerepresentationusingdeepautoencoderforlungnoduleimageclassification AT xinqiwang featurerepresentationusingdeepautoencoderforlungnoduleimageclassification AT weiyizhang featurerepresentationusingdeepautoencoderforlungnoduleimageclassification AT haoxiangwu featurerepresentationusingdeepautoencoderforlungnoduleimageclassification |