New artificial intelligence that diagnoses lung diseases with 98% accuracy

Researchers at the University of the West of Scotland (UWS) believe that with the help of artificial intelligence, lung diseases can be automatically diagnosed. Therefore, they have designed a system that is able to identify different lung diseases with high accuracy.

Identifying a group of lung diseases requires X-ray tests, ultrasound, CT scan, and blood tests, which may be time-consuming and expensive. Now, researchers from the University of the West of Scotland have developed an intelligent software that A few minutes can with an accuracy of approx 98 percent Diagnose several lung diseases. This software was initially developed for rapid diagnosis of corona infection from X-ray images.

Considering the continuation of the corona virus epidemic and the lack of staff in hospitals, scientists want this system to take at least part of the pressure off the treatment staff. Naeem Ramadan, a professor and researcher at the University of the West of Scotland, says:X-ray imaging It is a relatively inexpensive and readily available tool that helps diagnose a variety of diseases, including pneumonia, tuberculosis, and covid-19. Recent developments in the field Artificial intelligence It has made automatic detection through X-ray chest scans a very convenient option in clinics.”

How does the new artificial intelligence diagnose diseases?

Lung scan

This system receives X-ray images and compares them with a database of images of thousands of patients with pneumonia, tuberculosis and corona. Then with the help of a process called deep convolutional neural network (CNN), which is a type of machine learning for data processing, diagnoses the disease. This neural network is an algorithm capable of image analysis.

This technique has 98% accuracy in the experimental phase of research and has proven that it can be a suitable method for diagnosing lung diseases. Now the researchers are planning to see if this technique can be used to diagnose other diseases. The results of this study have been published in the journal Computer Methods and Programs in Biomedicine.

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