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https://hdl.handle.net/2440/135334
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Type: | Conference paper |
Title: | Lung nodule classification by jointly using visual descriptors and deep features |
Author: | Xie, Y. Zhang, J. Liu, S. Cai, W. Xia, Y. |
Citation: | Lecture Notes in Artificial Intelligence, 2017 / Muller, H., Kelm, B.M., Arbel, T., Cai, W., Cardoso, M.J., Langs, G., Menze, B., Metaxas, D., Montillo, A., Wells, W.M., Zhang, S., Chung, A.C.S., Jenkinson, M., Ribbens, A. (ed./s), vol.10081, pp.116-125 |
Publisher: | Springer International Publishing |
Publisher Place: | Cham, Switzerland |
Issue Date: | 2017 |
Series/Report no.: | Lecture Notes in Computer Science; 10081 |
ISBN: | 9783319611877 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | Medical Image Computing and Computer-Assisted Intervention (MICCAI) (21 Oct 2016 - 21 Oct 2016 : Athens, Greece) |
Editor: | Muller, H. Kelm, B.M. Arbel, T. Cai, W. Cardoso, M.J. Langs, G. Menze, B. Metaxas, D. Montillo, A. Wells, W.M. Zhang, S. Chung, A.C.S. Jenkinson, M. Ribbens, A. |
Statement of Responsibility: | Yutong Xie, Jianpeng Zhang, Sidong Liu, Weidong Cai, and Yong Xia |
Abstract: | Classifying benign and malignant lung nodules using the thoracic computed tomography (CT) screening is the primary method for early diagnosis of lung cancer. Despite of their widely recognized success in image classification, deep learning techniques may not achieve satisfying accuracy on this problem, due to the limited training samples resulted from the all-consuming nature of medical image acquisition and annotation. In this paper, we jointly use the texture and shape descriptors, which characterize the heterogeneity of nodules, and the features learned by a deep convolutional neural network, and thus proposed a combined-feature based classification (CFBC) algorithm to differentiate lung nodules. We have evaluated this algorithm against four state-of-the-art nodule classification approaches on the benchmark LIDC-IDRI dataset. Our results suggest that the proposed CFBC algorithm can distinguish malignant lung nodules from benign ones more accurately than other four methods. |
Keywords: | Lung module classification Computed tomography Deep convolutional neural network Texture descriptor Shape descriptor |
Rights: | © Springer International Publishing AG 2017 |
DOI: | 10.1007/978-3-319-61188-4_11 |
Published version: | https://link.springer.com/book/10.1007/978-3-319-61188-4 |
Appears in Collections: | Computer Science publications |
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