Full Text:   <7520>

Summary:  <1733>

CLC number: TP183; TP391.7

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2019-08-23

Cited: 0

Clicked: 7285

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Fu-li Wu

http://orcid.org/0000-0002-1566-9343

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.8 P.1161-1170

http://doi.org/10.1631/FITEE.1900210


Texture branch network for chronic kidney disease screening based on ultrasound images


Author(s):  Peng-yi Hao, Zhen-yu Xu, Shu-yuan Tian, Fu-li Wu, Wei Chen, Jian Wu, Xiao-nan Luo

Affiliation(s):  College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China; more

Corresponding email(s):   fuliwu@zjut.edu.cn

Key Words:  Chronic kidney disease, Ultrasound, Texture branch network, Transfer learning



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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