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

Peng-yi HAO; Zhen-yu XU; Shu-yuan TIAN; Fu-li WU; Wei CHEN; Jian WU; Xiao-nan LUO

doi:10.1631/FITEE.1900210

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

Regular Papers | Updated：2022-05-19

- Texture branch network for chronic kidney disease screening based on ultrasound images
  Enhanced Publication
- 纹理分支网络：基于超声影像的慢性肾脏病筛查模型
- Frontiers of Information Technology & Electronic Engineering Vol. 21, Issue 8, Pages: 1161-1170(2020)
- Affiliations：
  
  College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
  Tongde Hospital of Zhejiang Province, Hangzhou 310012, China
  The First Affiliated Hospital, Zhejiang University, Hangzhou 310003, China
  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
  Real Doctor AI Research Center, Zhejiang University, Hangzhou 310027, China
  Institute of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China
- Author bio：
  
  [ "", "Fu-li WU, corresponding author of this invited paper, is an associate professor at Zhejiang University of Technology. He received his PhD degree from Zhejiang University, China in 2005. From 2006 to 2007, he was a post-doctor of the University of Bedfordshire. From 2012 to 2013, he was a visiting scholar of the University of California-Davis. His research interests include computer graphics, data visualization, and medical image processing" ]
  [ "", "Prof. Jian WU, received a bachelor's degree and a PhD from College of Computer Science and Technology, Zhejiang University, China. He is the director of Real Doctor AI Research Center of Zhejiang University, member of CCF Youth Working Committee, CCF TCSC, and CCF TCAPP, one of the 151 Talents Program from Zhejiang Province, and a member of the key field innovation team of the Ministry of Science and Technology. His research interests focus on Artificial Intelligence in Medicine, Services Computing, and so on" ]
- Funds：
- DOI：10.1631/FITEE.1900210
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Peng-yi HAO, Zhen-yu XU, Shu-yuan TIAN, et al. Texture branch network for chronic kidney disease screening based on ultrasound images. [J]. Frontiers of Information Technology & Electronic Engineering 21(8):1161-1170(2020)
DOI：

Peng-yi HAO, Zhen-yu XU, Shu-yuan TIAN, et al. Texture branch network for chronic kidney disease screening based on ultrasound images. [J]. Frontiers of Information Technology & Electronic Engineering 21(8):1161-1170(2020) DOI： 10.1631/FITEE.1900210.

摘要

慢性肾脏病是一种在世界范围内广泛存在的肾脏疾病。该疾病一旦发展到晚期，伴随而来的是严重并发症与较高死亡风险。因此，早期筛查对于慢性肾脏病诊治至关重要。超声作为一种无创方法，能动态观察肾脏形态和病理特征，常用于肾脏检查。本文提出一种新的卷积神经网络模型，称为纹理分支网络，基于超声影像作慢性肾脏病筛查。该模型通过在经典卷积神经网络中引入纹理分支来提取和优化纹理特征，可自动生成输入图像的纹理特征和深度特征，并使用融合信息进行分类。此外，通过迁移学习训练网络的主干部分，并在具有226张超声影像的数据集上开展实验。实验结果表明，该模型准确率和敏感度分别达到96.01%和99.44%，在慢性肾脏病筛查上具有一定有效性。

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%.

关键词

慢性肾脏病超声纹理分支网络迁移学习

Keywords

Chronic kidney diseaseUltrasoundTexture branch networkTransfer learning

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