TY - GEN
T1 - Deep feature learning for pulmonary nodule classification in a lung CT
AU - Kim, Bum Chae
AU - Sung, Yu Sub
AU - Suk, Heung Il
N1 - 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.
PY - 2016/4/20
Y1 - 2016/4/20
N2 - 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.
AB - 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.
KW - Deep learning
KW - Lung cancer
KW - Pulmonary nodule classification
KW - Stacked denoising autoencoder
UR - http://www.scopus.com/inward/record.url?scp=84969175371&partnerID=8YFLogxK
U2 - 10.1109/IWW-BCI.2016.7457462
DO - 10.1109/IWW-BCI.2016.7457462
M3 - Conference contribution
AN - SCOPUS:84969175371
T3 - 4th International Winter Conference on Brain-Computer Interface, BCI 2016
BT - 4th International Winter Conference on Brain-Computer Interface, BCI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Winter Conference on Brain-Computer Interface, BCI 2016
Y2 - 22 February 2016 through 24 February 2016
ER -
