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

• URL: http://arxiv.org/abs/2205.08932v1
• Date: Wed, 18 May 2022 13:59:16 GMT
• Title: COVID-Net UV: An End-to-End Spatio-Temporal Deep Neural Network Architecture for Automated Diagnosis of COVID-19 Infection from Ultrasound Videos
• Authors: Hilda Azimi, Ashkan Ebadi, Jessy Song, Pengcheng Xi, Alexander Wong
• Abstract summary: COVID-Net comprises a convolutional neural network that extracts spatial features and a recurrent neural network that learns temporal dependence. The network achieves an average accuracy of 94.44% with no false-negative cases for COVID-19 cases.
• Score: 70.60433013657693
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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