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• W3212093023 abstract "Up to now, the coronavirus disease 2019 (COVID-19) has been sweeping across all over the world, which has affected individual’s lives in an overwhelming way. To fight efficiently against the COVID-19, radiography and radiology images are used by clinicians in hospitals. This article presents an integrated framework, named COVIDNet, for classifying COVID-19 patients and healthy controls. Specifically, ResNet (i.e., ResNet-18 and ResNet-50) is adopted as a backbone network to extract the discriminative features first. Second, the spatial pyramid pooling (SPP) layer is adopted to capture the middle-level features from the features of ResNet. To learn the high-level features, the NetVLAD layer is used to aggregate the features representation from middle-level features. The context gating (CG) mechanism is adopted to further learn the high-level features for predicting the COVID-19 patients or not. Finally, extensive experiments are conducted on the collected database, showing the excellent performance of the proposed integrated architecture, with the sensitivity up to 97% and specificity of 99.5% of the ResNet-18, and with the sensitivity up to 99% and specificity of 99.4% of the ResNet-50." @default.
• W3212093023 created "2021-11-22" @default.
• W3212093023 creator A5051916669 @default.
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• W3212093023 date "2022-07-01" @default.
• W3212093023 modified "2023-10-18" @default.
• W3212093023 title "COVIDNet: An Automatic Architecture for COVID-19 Detection With Deep Learning From Chest X-Ray Images" @default.
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• W3212093023 doi "https://doi.org/10.1109/jiot.2021.3126471" @default.
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