Abstract
The lack of medication or vaccination for new COVID-19 disease, the need for early detection of the infected people to be isolated immediately is of great importance in minimizing the chance of infection to a healthier community. The key screening tool for COVID-19 is RT-PCR, or blood specimens. Nonetheless, the average positive RT-PCR from throat-swab samples is estimated to be 30 to 60%, and then yields to undiagnosed cases, and may threaten a large community of healthy people with infectious symptoms. Radiography of the chest (e.g., Xray or CT imaging) as a standard method for diagnosing respiratory diseases is simple to perform with the fast examination procedure. Disease presence in these images was annotated by a board-certified radiologists. A subset of 2,000 X-rays was used to train four transfer learning approaches to COVID-19 disease detection, including ResNet-18, ResNet-50, SqueezeNet and DenseNet 121. We validated these models on the remaining 1,000 images and with ResNet-18 we achieved a sensitivity rate of 100% with a specificity rate of around 98.6%.
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Madhavi, K.R., Madhavi, G., Krishnaveni, C.V., Kora, P. (2021). COVID-19 Detection Using Deep Learning. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_26
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DOI: https://doi.org/10.1007/978-3-030-73050-5_26
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