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COVID-Net UV: An End-to-End Spatio-Temporal Deep Neural Network Architecture for Automated Diagnosis of COVID-19 Infection from Ultrasound Videos

2022-05-18 13:59:16
Hilda Azimi, Ashkan Ebadi, Jessy Song, Pengcheng Xi, Alexander Wong
arXiv_CV
       
Abstract

Besides vaccination, as an effective way to mitigate the further spread of COVID-19, fast and accurate screening of individuals to test for the disease is yet necessary to ensure public health safety. We propose COVID-Net UV, an end-to-end hybrid spatio-temporal deep neural network architecture, to detect COVID-19 infection from lung point-of-care ultrasound videos captured by convex transducers. COVID-Net UV comprises a convolutional neural network that extracts spatial features and a recurrent neural network that learns temporal dependence. After careful hyperparameter tuning, the network achieves an average accuracy of 94.44% with no false-negative cases for COVID-19 cases. The goal with COVID-Net UV is to assist front-line clinicians in the fight against COVID-19 via accelerating the screening of lung point-of-care ultrasound videos and automatic detection of COVID-19 positive cases.

Abstract (translated)

URL

https://arxiv.org/abs/2205.08932

PDF

https://arxiv.org/pdf/2205.08932.pdf

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