The website of the Clinical Services Journal

Machine learning helps develop more accurate COVID-19 diagnostic tool

​Researchers at the University of Maryland, Baltimore County (UMBC) have developed a method of generating high-quality chest X-ray images that can be used to diagnose COVID-19 more accurately than current methods.

The research team, led by Sumeet Menon, a Ph.D. student in computer science at UMBC, will publish its findings in the proceedings of the IEEE Big Data 2020 Conference

"The availability of data is one of the most important aspects of machine learning and our research has taken an incremental theoretical step towards generating data using the MTT-GAN," explains Menon.

The need for rapid and accurate COVID-19 testing is high, including testing that can determine if COVID-19 is impacting a patient's respiratory system. Many clinicians use x-ray technology to classify images of possible cases of COVID-19, but the limited data available makes it more challenging to classify those images accurately. 

Menon and his collaborators developed their tool as an extension of generative adversarial networks (GANs), machine learning frameworks that can quickly generate new data based on statistics from a training set. The team's more advanced method uses what they call Mean Teacher + Transfer Generative Adversarial Networks (MTT-GAN). The MTT-GANs, explains Menon, are superior to GANs because the images they generate are much more similar to authentic images generated by x-ray machines.

The MTT-GAN classification system has the potential to help improve the accuracy of COVID-19 classifiers, making it an important diagnostic tool for physicians who are still working to understand the range of ways this complex disease presents in patients. The paper mainly focuses on generating more COVID-19 x-rays using the MTT-GAN, which could be widely used to train machine learning models and could have many applications, including classification of CT-scans and segmentation.

 

Upcoming Events

AfPP Annual Conference 2022

University of York
8-11 September 2022

Infection 360: What's trending in infection prevention & control

Edgbaston Stadium, Birmingham
27-28 September 2022

IP2022 IS COMING TO BOURNEMOUTH IN OCTOBER 2022

Bournemouth
17-19 October 2022

UKHCA Conference: Listen Up

Pendulum Hotel and Manchester Conference Centre, Manchester
3rd November 2022

MEDICA 2022

Dusseldorf Germany
14th November - 17th November

Future Surgery 2022

ExCel, London
15th - 16th November 2022

Access the latest issue of Clinical Services Journal on your mobile device together with an archive of back issues.

Download the FREE Clinical Services Journal app from your device's App store

Upcoming Events

AfPP Annual Conference 2022

University of York
8-11 September 2022

Infection 360: What's trending in infection prevention & control

Edgbaston Stadium, Birmingham
27-28 September 2022

IP2022 IS COMING TO BOURNEMOUTH IN OCTOBER 2022

Bournemouth
17-19 October 2022

UKHCA Conference: Listen Up

Pendulum Hotel and Manchester Conference Centre, Manchester
3rd November 2022

MEDICA 2022

Dusseldorf Germany
14th November - 17th November

Future Surgery 2022

ExCel, London
15th - 16th November 2022

Access the latest issue of Clinical Services Journal on your mobile device together with an archive of back issues.

Download the FREE Clinical Services Journal app from your device's App store

Step Communications Ltd, Step House, North Farm Road, Tunbridge Wells, Kent TN2 3DR
Tel: 01892 779999 Fax: 01892 616177
www.step-communications.com
© 2022 Step Communications Ltd. Registered in England. Registration Number 3893025