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Emphysema Classification Using Convolutional Neural Networks

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Intelligent Robotics and Applications (ICIRA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9244))

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Abstract

There has been paid more and more attention in diagnosing emphysema using High-resolution Computed Tomography. This may lead to improve both understanding and computer-aided diagnosis. We propose a novel classification framework using convolutional neural network(CNN). This model automatically extracts features from the raw image and generates classification. Experiments have been conducted on the database from clinical. Results a recognition rate of 92.54% for classification two kinds of emphysema with normal. The designed convolutional neural networks can get better results for classifying one kind of emphysema with normal.

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Author information

Authors and Affiliations

  1. College of Information and Control Engineering, Liaoning Shihua University, Fushun, China

    Xiaomin Pei

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  1. Xiaomin Pei
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Correspondence to Xiaomin Pei .

Editor information

Editors and Affiliations

  1. University of Portsmouth, Portsmouth, United Kingdom

    Honghai Liu

  2. Tokyo Metropolitan University, Tokyo, Japan

    Naoyuki Kubota

  3. Shanghai Jiao Tong University, Shanghai, China

    Xiangyang Zhu

  4. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Rüdiger Dillmann

  5. University of Portsmouth, Portsmouth, United Kingdom

    Dalin Zhou

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Pei, X. (2015). Emphysema Classification Using Convolutional Neural Networks. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2015. Lecture Notes in Computer Science(), vol 9244. Springer, Cham. https://doi.org/10.1007/978-3-319-22879-2_42

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  • DOI: https://doi.org/10.1007/978-3-319-22879-2_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22878-5

  • Online ISBN: 978-3-319-22879-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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