AutoCovNet: Unsupervised feature learning using autoencoder and feature merging for detection of COVID-19 from chest X-ray images
Published 2021 View Full Article
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Title
AutoCovNet: Unsupervised feature learning using autoencoder and feature merging for detection of COVID-19 from chest X-ray images
Authors
Keywords
COVID-19 diagnosis, Medical Image Analysis, X-ray, Neural Network, Autoencoder
Journal
Biocybernetics and Biomedical Engineering
Volume 41, Issue 4, Pages 1685-1701
Publisher
Elsevier BV
Online
2021-10-22
DOI
10.1016/j.bbe.2021.09.004
References
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Related references
Note: Only part of the references are listed.- Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels
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- Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images
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- The pivotal link between ACE2 deficiency and SARS-CoV-2 infection
- (2020) Paolo Verdecchia et al. European Journal of Internal 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
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- Automated detection of COVID-19 cases using deep neural networks with X-ray images
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- (2020) Zhao Wang et al. IEEE Journal of Biomedical and Health Informatics
- CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images
- (2020) Emtiaz Hussain et al. CHAOS SOLITONS & FRACTALS
- Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
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- Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration
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