Deep feature learning for pulmonary nodule classification in a lung CT

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...

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Bibliographic Details
Published in:2016 4th International Winter Conference on Brain-Computer Interface (BCI) pp. 1 - 3
Main Authors: Bum-Chae Kim, Yu Sub Sung, Heung-Il Suk
Format: Conference Proceeding
Language:English
Published: IEEE 01.02.2016
Subjects:
Cancer
Computed tomography
Deep learning
Feature extraction
Lung cancer
Lungs
Noise reduction
Pulmonary nodule classification
Stacked denoising autoencoder
Training
ISBN:1467378410, 9781467378413
Online Access:Get full text
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Summary: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.
ISBN:1467378410
9781467378413
DOI:10.1109/IWW-BCI.2016.7457462