Author: Rojas-Azabache, Carlos; Vilca-Janampa, Karen; Guerrero-Huayta, Renzo; N'unez-Fern'andez, Dennis
Title: Detection of COVID-19 Disease using Deep Neural Networks with Ultrasound Imaging Cord-id: sl2p3ir4 Document date: 2021_4_4
ID: sl2p3ir4
Snippet: The new coronavirus 2019 (COVID-2019) has rapidly become a pandemic and has had a devastating effect on both everyday life, public health and the global economy. It is critical to detect positive cases as early as possible to prevent the further spread of this epidemic and to treat affected patients quickly. The need for auxiliary diagnostic tools has increased as accurate automated tool kits are not available. This paper presents a work in progress that proposes the analysis of images of lung u
Document: The new coronavirus 2019 (COVID-2019) has rapidly become a pandemic and has had a devastating effect on both everyday life, public health and the global economy. It is critical to detect positive cases as early as possible to prevent the further spread of this epidemic and to treat affected patients quickly. The need for auxiliary diagnostic tools has increased as accurate automated tool kits are not available. This paper presents a work in progress that proposes the analysis of images of lung ultrasound scans using a convolutional neural network. The trained model will be used on a Raspberry Pi to predict on new images.
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