Photos of toasters train AI 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.

Photo
Source: pixabay/OpenClipart-Vectors

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 Dr Shams Islam said this was incredibly useful for identifying and diagnosing emerging or uncommon medical conditions. "Our technique has the capacity to not only detect COVID-19 in chest x-rays, but also other chest diseases such as pneumonia. We have tested it on 10 different chest diseases, achieving highly accurate results," he said. "Normally, it is difficult for AI-based methods to perform detection of chest diseases accurately because the AI models need a very large amount of training data to understand the characteristic signatures of the diseases."

Islam added: "The data needs to be carefully annotated by medical experts, this is not only a cumbersome process, it also entails a significant cost. Our method bypasses this requirement and learns accurate models with a very limited amount of annotated data. While this technique is unlikely to replace the rapid COVID-19 tests we use now, there are important implications for the use of image recognition in other medical diagnoses."

Taking a shortcut on training

Lead author and ECU PhD candidate Fouzia Atlaf said the key to significantly decreasing the time needed to adapt the approach to other medical issues was pretraining the algorithm with the large ImageNet database. "ImageNet is a database of more than 1 million images which has been classified by humans - just like chest x-rays by medical professionals would need to be," she said. "The difference is the images in the database are of regular household items which can be classified by people without medical expertise."

Dr Islam and Ms Altaf hope the technique can be further refined in future research to increase accuracy and further reduce training time.

The research was published in Neural Computing and Applications.

Subscribe to our newsletter

Related articles

AI accurately detects COVID-19 on chest x-rays

AI accurately detects COVID-19 on chest x-rays

Researchers have developed a new AI platform that detects COVID-19 by analyzing X-ray images of the lungs.

Using machine learning to detect COVID-19 in X-rays

Using machine learning to detect COVID-19 in X-rays

Students at Cranfield University have designed computer models that can identify COVID-19 in X-rays.

COVID-19: AIs shortcuts lead to misdiagnosis

COVID-19: AIs shortcuts lead to misdiagnosis

Researchers discovered that AI models have a tendency to look for shortcuts. In the case of AI-assisted disease detection, these shortcuts could lead to diagnostic errors if deployed in clinical settings.

COVID-19: AI models not yet suitable for clinical use

COVID-19: AI models not yet suitable for clinical use

Researchers have found that out of the more than 300 COVID-19 machine learning models are not suitable for detecting or diagnosing COVID-19 from standard medical imaging.

5 ways AI is used against COVID-19

5 ways AI is used against COVID-19

Find out more about how scientists and physician are using AI to make contributions in the fight against the coronavirus.

AI distinguishes pneumonia from COVID-19

AI distinguishes pneumonia from COVID-19

Researchers have developed a predictive artificial intelligence model that can tell the difference between healthy patients, those who are ill with pneumonia and those who have COVID-19, from chest X-rays.

AI-based chest X-ray diagnosis tech approved

AI-based chest X-ray diagnosis tech approved

behold.ai has been issued with a CE Mark Class lla certification in the UK and EU for its AI-based technology that can diagnose chest X-rays as ‘normal’.

Ensembling improves machine learning model performance

Ensembling improves machine learning model performance

Ensembles created using models submitted to the RSNA Pediatric Bone Age Machine Learning Challenge convincingly outperformed single-model prediction of bone age.

AI predicts survival of ovarian cancer patients

AI predicts survival of ovarian cancer patients

Researchers have created new machine learning software that can forecast the survival rates and response to treatments of patients with ovarian cancer.

Popular articles

Subscribe to Newsletter