Full Text:   <1612>

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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: 3320

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


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.

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journal="Frontiers of Information Technology & Electronic Engineering",
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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%.

纹理分支网络:基于超声影像的慢性肾脏病筛查模型

郝鹏翼1,5,徐震宇1,田树元2,吴福理1,5,陈为3,5,吴健4,5,罗笑南6
1浙江工业大学计算机科学与技术学院,中国杭州市,310023
2浙江省立同德医院,中国杭州市,310012
3浙江大学附属第一医院,中国杭州市,310003
4浙江大学计算机科学与技术学院,中国杭州市,310027
5浙江大学睿医人工智能研究中心,中国杭州市,310027
6桂林电子科技大学人工智能学院,中国桂林市,541004

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。

关键词:慢性肾脏病;超声;纹理分支网络;迁移学习

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