X-rays, AI together may help detect Covid-19 faster
The findings, published in the IEEE/CAA Journal of Automatica Sinica, indicate that the research team used several different Machine Learning (ML) methods to detect Covid-19, two of which resulted
Rio De Janeiro: A team of researchers has found that X-rays could be a leading-edge diagnostic tool for Covid-19 patients with the help of Artificial Intelligence (AI).
The findings, published in the IEEE/CAA Journal of Automatica Sinica, indicate that the research team used several different Machine Learning (ML) methods to detect Covid-19, two of which resulted in a 95.6 per cent and a 98.5 per cent accuracy rating, respectively.
“We decided to investigate if a Covid-19 infection could be automatically detected using X-ray images,” said researcher Victor Hugo C. de Albuquerque from the Universidade de Fortaleza, noting that most X-ray images are available within minutes, compared to the days required for swab or saliva diagnostic tests.
However, the researchers found a lack of publicly available chest X-rays to train their AI model to automatically identify the lungs of Covid-19 patients.
They had just 194 Covid-19 X-rays and 194 healthy X-rays, while it usually takes thousands of images to thoroughly teach a model to detect and classify a particular target.
To compensate, the team took a model trained on a large dataset of other X-ray images and trained it to use the same methods to detect lungs likely infected with Covid-19.
“Since X-rays are very fast and cheap, they can help to triage patients in places where the health care system has collapsed or in places that are far from major centers with access to more complex technologies,” Albuquerque said.
“This approach to detect and classify medical images automatically can assist doctors in identifying, measuring the severity and classifying the disease,” Albuquerque added.
The researchers said they are planning to continue testing their method with larger datasets as they become available, with the ultimate goal of developing a free online platform for medical image classification.
