CLC number: TP183; TP391.7
On-line Access: 2020-08-10
Received: 2019-04-24
Revision Accepted: 2019-06-23
Crosschecked: 2019-08-23
Cited: 0
Clicked: 6021
Peng-yi Hao, Zhen-yu Xu, Shu-yuan Tian, Fu-li Wu, Wei Chen, Jian Wu, Xiao-nan Luo. Texture branch network for chronic kidney disease screening based on ultrasound images[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(8): 1161-1170.
@article{title="Texture branch network for chronic kidney disease screening based on ultrasound images",
author="Peng-yi Hao, Zhen-yu Xu, Shu-yuan Tian, Fu-li Wu, Wei Chen, Jian Wu, Xiao-nan Luo",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="8",
pages="1161-1170",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900210"
}
%0 Journal Article
%T Texture branch network for chronic kidney disease screening based on ultrasound images
%A Peng-yi Hao
%A Zhen-yu Xu
%A Shu-yuan Tian
%A Fu-li Wu
%A Wei Chen
%A Jian Wu
%A Xiao-nan Luo
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 8
%P 1161-1170
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900210
TY - JOUR
T1 - Texture branch network for chronic kidney disease screening based on ultrasound images
A1 - Peng-yi Hao
A1 - Zhen-yu Xu
A1 - Shu-yuan Tian
A1 - Fu-li Wu
A1 - Wei Chen
A1 - Jian Wu
A1 - Xiao-nan Luo
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 8
SP - 1161
EP - 1170
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900210
Abstract: chronic kidney disease (CKD) is a widespread renal disease throughout the world. Once it develops to the advanced stage, serious complications and high risk of death will follow. Hence, early screening is crucial for the treatment of CKD. Since ultrasonography has no side effects and enables radiologists to dynamically observe the morphology and pathological features of the kidney, it is commonly used for kidney examination. In this study, we propose a novel convolutional neural network (CNN) framework named the texture branch network to screen CKD based on ultrasound images. This introduces a texture branch into a typical CNN to extract and optimize texture features. The model can automatically generate texture features and deep features from input images, and use the fused information as the basis of classification. Furthermore, we train the base part of the network by means of transfer learning, and conduct experiments on a dataset with 226 ultrasound images. Experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy of 96.01% and a sensitivity of 99.44%.
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