Applying Attention Mechanism and Deep Neural Network for Medical Object Segmentation and Classification in X-Ray Fluoroscopy Images

2019 ◽  
pp. 101-110
Author(s):  
Yong Zhang ◽  
Jun Yan ◽  
Haitao Huang ◽  
Christopher Yencha
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Object Segmentation ◽  
Attention Mechanism ◽  
X Ray ◽  
Medical Object

scholarly journals An Automated Lightweight Deep Neural Network for Diagnosis of COVID-19 from Chest X-ray Images

2021 ◽  
Author(s):  
Soumya Ranjan Nayak ◽  
Janmenjoy Nayak ◽  
Utkarsh Sinha ◽  
Vaibhav Arora ◽  
Uttam Ghosh ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
X Ray ◽  
Chest X Ray

scholarly journals MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

2021 ◽  
Author(s):  
Liangrui Pan ◽  
boya ji ◽  
Xiaoqi wang ◽  
shaoliang peng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Test Accuracy ◽  
Learning Networks ◽  
X Ray ◽  
Network Algorithm ◽  
Life Saving ◽  
Chest X Ray ◽  
Neural Network Algorithm

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID-19) is life-saving important for both patients and doctors. This research proposed a multi-channel feature deep neural network algorithm to screen people infected with COVID-19. The algorithm integrates data oversampling technology and a multi-channel feature deep neural network model to carry out the training process in an end-to-end manner. In the experiment, we used a publicly available CXI database with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. Compared with traditional deep learning models (Densenet201, ResNet50, VGG19, GoogLeNet), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Secondly, compared with the latest CoroDet model, the MFDNN algorithm is 1.91% higher than the CoroDet model in the experiment of detecting the four categories of COVID19 infected persons. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

Deep Neural Network Guided by Attention Mechanism for Segmentation of Liver Pathology Image

2021 ◽  
pp. 425-435
Author(s):  
Zhongrui Zhai ◽  
Chaoli Wang ◽  
Zhanquan Sun ◽  
Shuqun Cheng ◽  
Kang Wang
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Attention Mechanism ◽  
Liver Pathology ◽  
Pathology Image

scholarly journals A Deep Neural Network for the Rapid Prediction of X-ray Absorption Spectra

2020 ◽  
Vol 124 (21) ◽  
pp. 4263-4270 ◽  
Author(s):  
C. D. Rankine ◽  
M. M. M. Madkhali ◽  
T. J. Penfold
Keyword(s):  
Neural Network ◽  
Absorption Spectra ◽  
Deep Neural Network ◽  
X Ray ◽  
Rapid Prediction ◽  
X Ray Absorption
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