Texture branch network for chronic kidney disease screening based on ultrasound images
| Autor: | Hao Pengyi, Tian Shuyuan, Wu Fuli, Wei Chen, Xiaonan Luo, Jian Wu, Xu Zhenyu |
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| Rok vydání: | 2019 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry Advanced stage Ultrasound Pattern recognition 02 engineering and technology Texture (music) medicine.disease Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Hardware and Architecture Signal Processing 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Risk of death Artificial intelligence Electrical and Electronic Engineering Ultrasonography business Transfer of learning Kidney disease |
| Zdroj: | Frontiers of Information Technology & Electronic Engineering. 21:1161-1170 |
| ISSN: | 2095-9230 2095-9184 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
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