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

by   Hilda Azimi, et al.

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 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.


page 1

page 2

page 3

page 4


An Approach Towards Physics Informed Lung Ultrasound Image Scoring Neural Network for Diagnostic Assistance in COVID-19

Ultrasound is fast becoming an inevitable diagnostic tool for regular an...

Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis

Controlling the COVID-19 pandemic largely hinges upon the existence of f...

Ultrasound Diagnosis of COVID-19: Robustness and Explainability

Diagnosis of COVID-19 at point of care is vital to the containment of th...

Lung Ultrasound Segmentation and Adaptation between COVID-19 and Community-Acquired Pneumonia

Lung ultrasound imaging has been shown effective in detecting typical pa...

Please sign up or login with your details

Forgot password? Click here to reset