Classification of optical coherence tomography images using a capsule network

Classification of optical coherence tomography images using a capsule network

Abstract Background Classification of optical coherence tomography (OCT) images can be achieved with high accuracy using classical convolution neural networks (CNN), a commonly used deep learning network for computer-aided diagnosis. Classical CNN has often been criticized for suppressing positional...

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Bibliographic Details
Main Authors: Takumasa Tsuji, Yuta Hirose, Kohei Fujimori, Takuya Hirose, Asuka Oyama, Yusuke Saikawa, Tatsuya Mimura, Kenshiro Shiraishi, Takenori Kobayashi, Atsushi Mizota, Jun’ichi Kotoku
Format: Article
Language:English
Published: BMC 2020-03-01
Series:BMC Ophthalmology
Subjects:
Capsule network
Choroidal neovascularization
Deep learning
Diabetic macular edema
Drusen
Optical coherence tomography
Online Access:http://link.springer.com/article/10.1186/s12886-020-01382-4

Internet

http://link.springer.com/article/10.1186/s12886-020-01382-4

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