Student team develops tool for diagnosing COVID-19 from X-rays

A team of students at Cranfield University in the UK have designed computer models that can identify COVID-19 in X-rays.

COVID-19 X-rays
The team of students collaborated on the project whilst working remotely from their respective home countries. From L to R: Shuozhi Wang, Thierry Vove, Yiguan Gao.

The models use computer vision and artificial intelligence (AI) to analyse chest X-ray imagery for signs of pneumonia – a common symptom of COVID-19 – and to determine whether this is caused by the COVID-19 virus.

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The technology has been developed by two groups studying for their MSc programme, specialising in Computer and Machine Vision. Due to lockdown, the groups carried out the projects remotely, and were able to use video conference and IT facilities provided by the University to access the computational resources required.

The groups employed conventional machine learning algorithms as well as deep learning frameworks, a machine learning technique that teaches computers to learn by example. The AI model employed in this project was able to predict results with great accuracy. However, the research teams believe that they can further develop new algorithms to produce even more robust and reliable results.

The teams are led by Dr Zeeshan Rana, Lecturer in Computational Engineering at Cranfield University. He is now exploring collaboration opportunities with medical authorities or industry to develop the Covid-19 x-rays project to the next level, using more advanced AI algorithms and CT (computed tomography) scans to show greater detail and accuracy in the results.

Dr Zeeshan Rana said: “The research carried out in this pilot project has led to some extremely promising results and we are looking to build on this success rapidly to help in the fight against COVID-19. I am incredibly proud of the work my researchers have carried out. They are a credit to the University and I’m delighted that we are able to support them remotely in carrying out their studies.”

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