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Journal of Healthcare Engineering
Volume 2017, Article ID 5859727, 20 pages
https://doi.org/10.1155/2017/5859727
Research Article

Deep Learning- and Transfer Learning-Based Super Resolution Reconstruction from Single Medical Image

1School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
2College of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300222, China
3College of Science, Tianjin University of Science and Technology, Tianjin 300222, China

Correspondence should be addressed to YiNan Zhang; moc.361@n.y.hz

Received 23 January 2017; Revised 5 April 2017; Accepted 10 May 2017; Published 6 July 2017

Academic Editor: Shujun Fu

Copyright © 2017 YiNan Zhang and MingQiang An. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Medical images play an important role in medical diagnosis and research. In this paper, a transfer learning- and deep learning-based super resolution reconstruction method is introduced. The proposed method contains one bicubic interpolation template layer and two convolutional layers. The bicubic interpolation template layer is prefixed by mathematics deduction, and two convolutional layers learn from training samples. For saving training medical images, a SIFT feature-based transfer learning method is proposed. Not only can medical images be used to train the proposed method, but also other types of images can be added into training dataset selectively. In empirical experiments, results of eight distinctive medical images show improvement of image quality and time reduction. Further, the proposed method also produces slightly sharper edges than other deep learning approaches in less time and it is projected that the hybrid architecture of prefixed template layer and unfixed hidden layers has potentials in other applications.