In a new development against the continued fight with COVID-19, a method of producing high-quality chest X-rays scans developed. These scans can be used to detect COVID-19 more accurately than currently used methods. The finding is scheduled to be demonstrated at the IEEE Big Data 2020 Conference of December 2020.
“Meanwhile, the availability of data is one of the most important elements of machine learning. By means of this, the research has taken a theoretical step forward for generating data using MTT-GAN,” explains the lead researcher.
In fact, the need for rapid and precise testing of COVID-19 is high, which includes testing to determine if COVID-19 is impacting the respiratory system of a patient. Currently, X-ray technology is used by many clinicians to distinguish scans of possible cases of COVID-19, however, data available is limited, and thus makes it challenging to accurately classify these images.
New tool superior than currently used one
Interestingly, the tool developed by the team of researchers at the University of Maryland is an extension of generative adversarial networks. Elaborately, generative adversarial networks are machine learning frameworks that quickly generate new data based on statistics from a training set. For extending this, the team uses more advanced method what is called Mean Teacher combined with Transfer Generative Adversarial Networks.
Following this, the lead research explains why Mean Teacher combined with Transfer Generative Adversarial Networks is superior to generative adversarial networks. This is because the former generates images that are much more similar to original scans produced by X-ray machines.
Furthermore, the MTT-GAN system has the potential for improving the accuracy of COVID-19 classifying scans. This makes it an important diagnostic instrument for physicians who are still striving to understand in the number of ways this complex disease is present in patients.