A deep learning neural network can quickly detect COVID-19 infections using X-ray images.
The deep learning neural network named CORONA-Net was developed by scientists at The University of British Columbia (Kelowna, BC Canada) to help doctors who lack access to polymerase chain reaction (PCR) tests and need a way to rapidly screen patients for COVID-19. As COVID-19 continues to make headlines across the globe, people have become used to the idea of rapid testing to determine if they have been infected. The viral test only indicates if a current infection exists, but not if there was previous infection. The alternative antibody test uses a blood sample and can detect if there was a previous infection with the SARS-CoV-2 virus, even if there are no current symptoms. However, the PCR test can be rare in many countries and usually costs several hundred dollars each time. Doctors around the world need a way to rapidly test patients for COVID-19 so that they can begin immediate treatment for patients with the virus
UBC Okanagan researchers, who say rapid tests can be limited and expensive in many countries, are testing another testing method. And they believe, thanks to artificial intelligence, they have found one. The research team has developed CORONA-Net, a deep learning neural network that can quickly detect COVID-19 infections using X-ray images. In many countries, people opt for chest X-ray because of the cost of a PCR test or its unavailability. However, sometimes it is difficult to get the X-ray looked at by a specialist, and accurately detecting the infection can take time. But by using CORONA-NET, the artificial intelligence system can flag suspicious cases to be fast-tracked and looked at quickly.
The developed CORONA-Net architecture substantially increases the sensitivity and positive predictive value (PPV) of predictions, making CORONA-Net a valuable tool when it comes to using chest X-rays to diagnose COVID-19. According to the researchers, the developed CORONA-Net was able to produce results with an accuracy of more than 95% in classifying COVID-19 cases from digital chest X-ray images. The accuracy of detecting COVID-19 by CORONA-Net will continue to increase as the dataset grows. CORONA-Net can automatically improve itself over time and self-learn to be more accurate.
“COVID-19 typically causes pneumonia in human lungs, which can be detected in X-ray images. These datasets of X-rays - of people with pneumonia inflicted by COVID-19, of people with pneumonia inflicted by other diseases, as well as X-rays of healthy people - allow the possibility to create deep learning networks that can differentiate between images of people with COVID-19 and people who do not have the disease,” said graduate student Sherif Elbishlawi, who helped develop CORONA-Net.
“The results on the testing set were obtained and can be seen in 100 per cent sensitivity to the COVID-19 class. There was a 95% sensitivity in the classification of the pneumonia class and a 95 per cent sensitivity in the classification of the normal class,” he added. “These results show that CORONA-Net gives a highly accurate prediction with the most sensitivity to the COVID-19 class.”
The University of British Columbia