Covid-19 detection via deep neural network and occlusion sensitivity maps
Published 2021 View Full Article
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Title
Covid-19 detection via deep neural network and occlusion sensitivity maps
Authors
Keywords
Covid-19, Pneumonia, Deep neural networks, Occlusion sensitivity maps
Journal
Alexandria Engineering Journal
Volume 60, Issue 5, Pages 4829-4855
Publisher
Elsevier BV
Online
2021-03-30
DOI
10.1016/j.aej.2021.03.052
References
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Note: Only part of the references are listed.- Developing an Efficient Deep Neural Network for Automatic Detection of COVID-19 Using Chest X-ray Images
- (2021) Sobhan Sheykhivand et al. Alexandria Engineering Journal
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- (2020) Charmaine Butt et al. APPLIED INTELLIGENCE
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- (2020) Alireza Tahamtan et al. EXPERT REVIEW OF MOLECULAR DIAGNOSTICS
- Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT
- (2020) Lin Li et al. RADIOLOGY
- Lower mortality of COVID-19 by early recognition and intervention: experience from Jiangsu Province
- (2020) Qin Sun et al. Annals of Intensive Care
- COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach
- (2020) Gergo Pinter et al. Mathematics
- Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review
- (2020) Adam Jacobi et al. CLINICAL IMAGING
- Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks
- (2020) Ali Abbasian Ardakani et al. COMPUTERS IN BIOLOGY AND MEDICINE
- CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization
- (2020) Tanvir Mahmud et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Automated detection of COVID-19 cases using deep neural networks with X-ray images
- (2020) Tulin Ozturk et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets
- (2020) Yujin Oh et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report
- (2020) Jihad S Obeid et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- New machine learning method for image-based diagnosis of COVID-19
- (2020) Mohamed Abd Elaziz et al. PLoS One
- The role of chest radiography in confirming covid-19 pneumonia
- (2020) Joanne Cleverley et al. BMJ-British Medical Journal
- COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization
- (2020) Tahmina Zebin et al. APPLIED INTELLIGENCE
- An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data
- (2020) Yaohao Peng et al. CHAOS SOLITONS & FRACTALS
- Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial
- (2020) Hoyt Burdick et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study
- (2020) Soumya Ranjan Nayak et al. Biomedical Signal Processing and Control
- Deep learning approaches for COVID-19 detection based on chest X-ray images
- (2020) Aras M. Ismael et al. EXPERT SYSTEMS WITH APPLICATIONS
- Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks
- (2016) Yushi Chen et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- ImageNet Large Scale Visual Recognition Challenge
- (2015) Olga Russakovsky et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
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