Researchers are using photos of toasters and fridges to train algorithms to detect COVID
New research using machine learning on images of everyday items is improving the accuracy and speed of detecting respiratory diseases, reducing the need for specialist medical expertise
Results of this technique, known as transfer learning, achieved a 99.24 per cent success rate when detecting COVID-19 in chest x-rays.
The study tackles one of the biggest challenges in image recognition machine learning: algorithms needing huge quantities of data, in this case images, to be able to recognise certain attributes accurately.
ECU School of Science researcher END
The study tackles one of the biggest challenges in image recognition machine learning: algorithms needing huge quantities of data, in this case images, to be able to recognise certain attributes accurately.
ECU School of Science researcher END