Using Parallel Convolutional Neural Networks for Treatment Position Recognition in X-ray Images.
10.3969/j.issn.1671-7104.2018.02.004
- Author:
Lei GUO
1
;
Hongwei HE
1
;
Yujun WANG
1
;
Changyuan WANG
1
;
Xiuyun YANG
1
;
Lu LIU
1
Author Information
1. Taishan Medical University, Tai'an, 271016.
- Publication Type:Journal Article
- Keywords:
image feature;
medical image processing;
parallel convolutional neural networks;
treatment position recognition
- MeSH:
Algorithms;
Image Processing, Computer-Assisted;
Neural Networks (Computer);
X-Rays
- From:
Chinese Journal of Medical Instrumentation
2018;42(2):92-94
- CountryChina
- Language:Chinese
-
Abstract:
Treatment position recognition in medical images is a key technique in medical image processing. Due to the excellent performance of convolutional neural networks on features extraction and classification, an architecture of parallel convolutional neural networks is proposed to recognize treatment positions in X-ray images, which uses convolution kernels of different sizes to extract local features of different sizes in these images. The experimental analysis shows that parallel convolution neural networks, which can extract representative image features with more dimensions, are competent to classify and recognize treatment positions in medical images.
