COVIDNet: An Automatic Architecture for COVID-19 Detection with Deep Learning from Chest X-ray Images

Up to now, the 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 paper 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. 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.

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Recommended citation: L. He, P. Tiwari, R. Su, X. Shi, P. Marttinen and N. Kumar, “COVIDNet: An Automatic Architecture for COVID-19 Detection with Deep Learning from Chest X-ray Images,” in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2021.3126471.