scholarly journals Image-based Motion Artifact Reduction on Liver Dynamic Contrast Enhanced MRI

2021 ◽  
Author(s):  
Yunan Wu ◽  
Junchi Liu ◽  
Gregory M White ◽  
Jie Deng
Keyword(s):  
Deep Learning ◽  
Motion Artifact ◽  
Learning Model ◽  
Motion Artifacts ◽  
Stage I ◽  
Stage Ii ◽  
Two Stage ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Deep Learning Model

AbstractLiver MRI images often suffer degraded quality from ghosting or blurring artifact caused by patient respiratory or bulk motion. In this study, we developed a two-stage deep learning model to reduce motion artifact on dynamic contrast enhanced (DCE) liver MRIs. The stage-I network utilized a deep residual network with a densely connected multi-resolution block (DRN-DCMB) network to remove the majority of motion artifacts. The stage-II network applied the perceptual loss to preserve image structural features by updating the parameters of the stage-I network via backpropagation. The stage-I network was trained using small image patches simulated with five types of motion, i.e., rotational, sinusoidal, random, elastic deformation and through-plane, to mimic actual liver motion patterns. The stage-II network training used full-size images with the same types of motion as the stage-I network. The motion reduction deep learning model was testing using simulated motion images and images with real motion artifacts. The resulted images after two-stage processing demonstrated substantially reduced motion artifacts while preserved anatomic details without image blurriness. This model outperformed existing methods of motion reduction artifact on liver DCE-MRI.

scholarly journals Development of a deep learning model for classifying thymoma as Masaoka-Koga stage I or II via preoperative CT images

2020 ◽  
Vol 8 (6) ◽  
pp. 287-287
Author(s):  
Lei Yang ◽  
Wenjia Cai ◽  
Xiaoyu Yang ◽  
Haoshuai Zhu ◽  
Zhenguo Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Ct Images ◽  
Stage I ◽  
Preoperative Ct ◽  
Deep Learning Model

scholarly journals TSDL: A Two-Stage Deep Learning Model for Efficient Network Intrusion Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 30373-30385 ◽  
Author(s):  
Farrukh Aslam Khan ◽  
Abdu Gumaei ◽  
Abdelouahid Derhab ◽  
Amir Hussain
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Learning Model ◽  
Network Intrusion Detection ◽  
Two Stage ◽  
Network Intrusion ◽  
Deep Learning Model

A generic deep learning model for reduced gadolinium dose in contrast‐enhanced brain MRI

2021 ◽  
Author(s):  
Srivathsa Pasumarthi ◽  
Jonathan I. Tamir ◽  
Soren Christensen ◽  
Greg Zaharchuk ◽  
Tao Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Learning Model ◽  
Contrast Enhanced ◽  
Deep Learning Model

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Sign in / Sign up

Export Citation Format

Share Document