MLCA2F: Multi-Level Context Attentional Feature Fusion for COVID-19 lesion segmentation from CT scans

Ibtissam Bakkouri; Karim Afdel

doi:10.1007/s11760-022-02325-w

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MLCA2F: Multi-Level Context Attentional Feature Fusion for COVID-19 lesion segmentation from CT scans

Ibtissam Bakkouri, Karim Afdel

Springer Science+Business Media (2022) • Volume 17, Issue 4, Pages 1181-1188

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Publication Info

DOI

10.1007/s11760-022-02325-w

Language

English

Topic

COVID-19 diagnosis using AI

Keywords

Context (archaeology), Artificial intelligence, Feature (linguistics), Segmentation, Computer science, Pattern recognition (psychology), Deep learning, Market segmentation, Coronavirus disease 2019 (COVID-19), Ambiguity, Computer vision, Machine learning, Medicine, Geography, Disease

Published

August 3, 2022

Citations

116 citations

FWCI

22.65

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MLCA2F: Multi-Level Context Attentional Feature Fusion for COVID-19 lesion segmentation from CT scans

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