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Deep Learning in Medical Image Analysis pp 47–58Cite as

Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation

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Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1213))

Abstract

Image-based computer-aided diagnosis (CAD) algorithms by the use of convolutional neural network (CNN) which do not require the image-feature extractor are powerful compared with conventional feature-based CAD algorithms which require the image-feature extractor for classification of lung abnormalities. Moreover, computer-aided detection and segmentation algorithms by the use of CNN are useful for analysis of lung abnormalities. Deep learning will improve the performance of CAD systems dramatically. Therefore, they will change the roles of radiologists in the near future. In this article, we introduce development and evaluation of such image-based CAD algorithms for various kinds of lung abnormalities such as lung nodules and diffuse lung diseases.

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Author information

Authors and Affiliations

  1. Graduate School of Medicine, Osaka University, Suita, Osaka, Japan

    Shoji Kido

  2. Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube, Yamaguchi, Japan

    Yasushi Hirano & Shingo Mabu

Authors
  1. Shoji Kido
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  2. Yasushi Hirano
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  3. Shingo Mabu
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Corresponding author

Correspondence to Shoji Kido .

Editor information

Editors and Affiliations

  1. College of Sciences & Engineering, Flinders University, Adelaide, SA, Australia

    Gobert Lee

  2. Faculty of Engineering, Gifu University, Gifu, Japan

    Hiroshi Fujita

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Kido, S., Hirano, Y., Mabu, S. (2020). Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation. In: Lee, G., Fujita, H. (eds) Deep Learning in Medical Image Analysis . Advances in Experimental Medicine and Biology, vol 1213. Springer, Cham. https://doi.org/10.1007/978-3-030-33128-3_3

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