Pre-trained quantum convolutional neural network for COVID-19 disease classification using computed tomography images

Our study presents pre-trained QCNN models as a viable technique for COVID-19 disease detection, showcasing their effectiveness in reaching higher accuracy and specificity. The current paper adds to the continuous efforts to utilize artificial intelligence to aid healthcare professionals in the diagnosis of COVID-19 patients.

Authors

Publication Details

Journal
PeerJ Computer Science
Published
2024-10-18
DOI
https://doi.org/10.7717/peerj-cs.2343
Citations
4
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
1.62
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article

Pre-trained quantum convolutional neural network for COVID-19 disease classification using computed tomography images

Shokufeh Yaraghi, Nazeh Asadoorian, Araeek Tahmasian
4 citations
PeerJ Computer Science
COVID-19 diagnosis using AI
1.62
article

Pre-trained quantum convolutional neural network for COVID-19 disease classification using computed tomography images

Shokufeh Yaraghi, Nazeh Asadoorian, Araeek Tahmasian
article en
4 citations

Abstract

Our study presents pre-trained QCNN models as a viable technique for COVID-19 disease detection, showcasing their effectiveness in reaching higher accuracy and specificity. The current paper adds to the continuous efforts to utilize artificial intelligence to aid healthcare professionals in the diagnosis of COVID-19 patients.

PeerJ Computer ScienceVol. 10
Good health and well-being
Openalex Percentile: Top 16%
COVID-19 diagnosis using AI
1.62
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Pre-trained quantum convolutional neural network for COVID-19 disease classification using computed tomography images — Shokufeh Yaraghi, Nazeh Asadoorian, et al. · PeerJ Computer Science (2024) | TGRS Research Map | TGRS