Investigation of a Recognition System for General X-ray Images Using CNN and Faster R-CNN-Reference-Cited by-同舟云学术

Investigation of a Recognition System for General X-ray Images Using CNN and Faster R-CNN

Published:2022-11-01 Issue:6 Volume:26 Page:159-169
ISSN:1342-6230
Container-title:Journal of Signal Processing
language:en
Short-container-title:Journal of Signal Processing

Author:

Matsushima Akari¹,Chen Tai-Been²,Hsu Shih-Yen³,Okamoto Takahide¹

Affiliation:

1. Department of Radiological Technology, Faculty of Medical Technology, Graduate School of Medical Care and Technology, Teikyo University

2. Department of Medical Imaging and Radiological Sciences, I-Shou University

3. Department of Information Engineering, I-Shou University

Publisher

Research Institute of Signal Processing, Japan

Subject

General Energy

Link

https://www.jstage.jst.go.jp/article/jsp/26/6/26_159/_pdf

Reference11 articles.

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2. [2] ] Japan Network for Research and Information on Medical Exposure (J-RIME): National Diagnostic Reference Levels in Japan (2020) - Japan DRLs 2020-. http://www.radher.jp/J-RIME/report/DRL2020_Engver.pdf, 2020.

3. [3] H. Munker: Chromatic grids, projection to the retina, and translation theory-based description of the color perception, Habilitation thesis, Ludwig-Maximilians University, Munich, 1970.

4. [4] K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.

5. [5] A. Matsushima, C. Tai-Been, T. Okamoto, et al.: Basic study of a knee joint radiography positioning determination system using convolutional neural network transfer learning, Poster presented at European Congress of Radiology, 2021 (online).

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