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A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images
inferredcites →https://doi.org/10.1080/07391102.2020.1788642openalex-snapshot
Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning
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Emergency Risk Communication: Lessons Learned from a Rapid Review of Recent Gray Literature on Ebola, Zika, and Yellow Fever
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PCA versus LDA
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Blockchain-Empowered Multi-Robot Collaboration to Fight COVID-19 and Future Pandemics
inferredcites →https://doi.org/10.1109/jbhi.2018.2872038openalex-snapshot
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inferredcites →https://doi.org/10.1109/jbhi.2020.3037127openalex-snapshot
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inferredcites →https://doi.org/10.1109/tai.2021.3115093openalex-snapshot
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inferredcites →https://doi.org/10.1109/tcbb.2020.3009859openalex-snapshot
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inferredcites →https://doi.org/10.1109/tmi.2020.2993291openalex-snapshot
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets
inferredcites →https://doi.org/10.1109/tsg.2016.2544947openalex-snapshot
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inferredcites →https://doi.org/10.21437/interspeech.2020-2768openalex-snapshot
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