BEMD-3DCNN-based method for COVID-19 detection
Open Access
- 29 December 2021
- journal article
- research article
- Published by Elsevier BV in Computers in Biology and Medicine
- Vol. 142, 105188
- https://doi.org/10.1016/j.compbiomed.2021.105188
Abstract
No abstract availableKeywords
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Funding Information
- Qatar University (QUERG-CENG-2020-1)
This publication has 46 references indexed in Scilit:
- Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19IEEE Reviews in Biomedical Engineering, 2021
- Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer LearningSymmetry, 2020
- Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networksPhysical and Engineering Sciences in Medicine, 2020
- Re-epithelialization and immune cell behaviour in an ex vivo human skin modelScientific Reports, 2020
- Pyramid Feature Attention Network for Saliency DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2019
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
- Moving object detection using a background modeling based on entropy theory and quad-tree decompositionJournal of Electronic Imaging, 2016
- BEMD–SIFT feature extraction algorithm for image processing applicationMultimedia Tools and Applications, 2016
- Enhancing Image Denoising Performance of Bidimensional Empirical Mode Decomposition by Improving the Edge EffectInternational Journal of Antennas and Propagation, 2015
- Distinctive Image Features from Scale-Invariant KeypointsInternational Journal of Computer Vision, 2004