Performance Evaluation of Learning Models for the Prognosis of COVID-19
Abstract
References
Recommendations
Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio
AbstractThe novel 2019 coronavirus disease (COVID-19) has infected over 141 million people worldwide since April 20, 2021. More than 200 countries around the world have been affected by the coronavirus pandemic. Screening for COVID-19, we use ...
Highlights- To propose two efficient COVID-19 classification models based on convolutional neural network and convolutional auto-encoder neural network for ...
Classification of COVID-19 on Chest X-Ray Images Using Deep Learning Model with Histogram Equalization and Lung Segmentation
AbstractArtificial intelligence techniques coupled with biomedical analysis have been play a critical role during COVID-19 pandemics as it helps to release the overwhelming pressure from healthcare systems and physicians. As the ongoing COVID-19 crisis ...
A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications
Highlights- Convolution neural network used for the chest computed tomography pictures for the screening of COVID 19 in this paper.
AbstractThe Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Publisher
Ohmsha
Japan
Publication History
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in