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Lung nodules detection based on modified extreme learning machine with deep convolutional features
Author(s): Guodong Zhang; Yuxuan Sun; Lingchuang Kong; Jing Bi; Zhaoxuan Gong; Yoohwan Kim; Wei Guo
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Paper Abstract

This work achieves a method based on modified extreme learning machine (ELM) with deep convolutional features to detect lung nodules automatically. Convolutional neural networks (CNNs) are employed to extract the features of lung nodules for classification. And then ELM is used to detect the lung nodules by combining the normalization and vote selection. In comparison with the traditional methods, it is shown that our method achieves a higher performance and it can be used as an effective tool for lung nodules computer aided diagnosis.

Paper Details

Date Published: 9 August 2018
PDF: 7 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080657 (9 August 2018); doi: 10.1117/12.2502829
Show Author Affiliations
Guodong Zhang, Shenyang Aerospace Univ. (China)
Univ. of Nevada, Las Vegas (United States)
Yuxuan Sun, Shenyang Aerospace Univ. (China)
Lingchuang Kong, Shenyang Aerospace Univ. (China)
Jing Bi, Shenyang Aerospace Univ. (China)
Zhaoxuan Gong, Shenyang Aerospace Univ. (China)
Yoohwan Kim, Univ. of Nevada, Las Vegas (United States)
Wei Guo, Shenyang Aerospace Univ. (China)
Shenyang Institute of Computing Technology (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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