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

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First Online:
Part of the Advances in Experimental Medicine and Biology book series (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.

Keywords

Computer-aided diagnosis (CAD) Convolutional neural network (CNN) R-CNN Fully convolutional network (FCN) U-Net Residual U-Net V-Net Lung nodule Diffuse lung disease 
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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Graduate School of Medicine, Osaka UniversitySuitaJapan
  2. 2.Graduate School of Sciences and Technology for Innovation, Yamaguchi UniversityUbeJapan

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