Nakagami分布
卷积神经网络
接收机工作特性
模式识别(心理学)
人工智能
参数统计
计算机科学
纤维化
特征(语言学)
深度学习
参数化模型
肝纤维化
特征提取
数学
医学
病理
统计
算法
机器学习
衰退
哲学
解码方法
语言学
作者
Qiang Liu,Zhong Liu,Wencong Xu,Huiying Wen,Ming Dai,Xin Chen
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2021-01-01
卷期号:9: 89300-89310
被引量:7
标识
DOI:10.1109/access.2021.3064879
摘要
The assessment of liver fibrosis is usually required in the diagnostic and treatment procedures for chronic liver disease. In this paper, we propose a deep convolutional neural network (DCNN) with multi-feature fusion (DCNN-MFF) to extract and integrate features from ultrasound (US) B-mode image and Nakagami parametric map for significant liver fibrosis recognition. The DCNN-MFF model mainly consists of three branches, among which the first two branches are used for extracting deep features respectively from the US B-mode image and the Nakagami parametric map, while the remaining branch is designed for extracting quantitative US features from the Nakagami parametric map. At the backend of the DCNN model, the extracted deep and quantitative features are fused together for final decision making. The performance of the DCNN-MFF model was evaluated on an animal dataset collected from 84 rats with 168 liver lobes under various fibrosis stages. Across five-fold cross-validation, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) achieved by the proposed DCNN-MFF model for significant liver fibrosis recognition were respectively 0.827 (95% confidence interval [CI] 0.762-0.881), 0.821 (95% CI 0.729-0.892), 0.836 (95% CI 0.731-0.912), 0.869 (95% CI 0.809-0.916), which are significantly better than those provided by the comparative methods.
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