Abstract
In this paper, we propose a novel method of identifying pulmonary nodules in a lung CT. Specifically, we devise a deep neural network by which we extract abstract information inherent in raw hand-crafted imaging features. We then combine the deep learned representations with the original raw imaging features into a long feature vector. By taking the combined feature vectors, we train a classifier, preceded by a feature selection via t-test. To validate the effectiveness of the proposed method, we performed experiments on our in-house dataset of 20 subjects; 3,598 pulmonary nodules (malignant: 178, benign: 3,420), which were manually segmented by a radiologist. In our experiments, we achieved the maximal accuracy of 95.5%, sensitivity of 94.4%, and AUC of 0.987, outperforming the competing method.
Original language | English |
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Title of host publication | 4th International Winter Conference on Brain-Computer Interface, BCI 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781467378413 |
DOIs | |
Publication status | Published - 2016 Apr 20 |
Event | 4th International Winter Conference on Brain-Computer Interface, BCI 2016 - Gangwon Province, Korea, Republic of Duration: 2016 Feb 22 → 2016 Feb 24 |
Publication series
Name | 4th International Winter Conference on Brain-Computer Interface, BCI 2016 |
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Other
Other | 4th International Winter Conference on Brain-Computer Interface, BCI 2016 |
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Country/Territory | Korea, Republic of |
City | Gangwon Province |
Period | 16/2/22 → 16/2/24 |
Bibliographical note
Funding Information:This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1C1A1A01052216). The authors gratefully acknowledge technical supports from Biomedical Imaging Infrastructure, Department of Radiology, Asan Medical Center.
Publisher Copyright:
© 2016 IEEE.
Keywords
- Deep learning
- Lung cancer
- Pulmonary nodule classification
- Stacked denoising autoencoder
ASJC Scopus subject areas
- Human-Computer Interaction