Journal Home Online First Current Issue Archive For Authors Journal Information 中文版

2020, Volume 21, Issue 8

Abstract

Keywords

Related Research

Back to Top

Frontiers of Information Technology & Electronic Engineering >> 2020, Volume 21, Issue 8 doi: 10.1631/FITEE.1900210

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

Affiliation(s): 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; less

Received: 2019-04-24 Accepted: 2020-08-10 Available online: 2020-08-10
HTML29 PDF 14 Collect 0

Next Previous

Abstract

(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 to screen CKD based on 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 , and conduct experiments on a dataset with 226 images. Experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy of 96.01% and a sensitivity of 99.44%.

Related Research