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Analysis of the effects of transfer learning on opacity classification of diffuse lung diseases using convolutional neural network
Author(s): Ami Atsumo; Shingo Mabu; Shoji Kido; Yasushi Hirano; Takashi Kuremoto
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Paper Abstract

Research on Computer-Aided Diagnosis (CAD), which discriminates the presence or absence of diseases by machine learning and supports doctors’ diagnosis, has been actively conducted. However, training of machine learning requires many training data with annotations. Since the annotations are done by radiologists manually, annotating hundreds to thousands of images is very hard work. This study proposes classifiers using convolutional neural network (CNN) with transfer learning for efficient opacity classification of diffuse lung diseases, and the effects of transfer learning are analyzed under various conditions. In detail, classifiers with nine different conditions of transfer learning and without transfer learning are compared to show the best conditions.

Paper Details

Date Published: 27 March 2019
PDF: 5 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 1105017 (27 March 2019); doi: 10.1117/12.2521229
Show Author Affiliations
Ami Atsumo, Yamaguchi Univ. (Japan)
Shingo Mabu, Yamaguchi Univ. (Japan)
Shoji Kido, Yamaguchi Univ. (Japan)
Yasushi Hirano, Yamaguchi Univ. (Japan)
Takashi Kuremoto, Yamaguchi Univ. (Japan)


Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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