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Engineering researchers Dr Abbas Kouzani and Alycia Lee have developed an automated system to improve the vital early detection of lung cancer—one of the most common cancers in Australia.
Lung disease, including cancer, is usually detected with the aid of CT (computed tomography) and MRI (magnetic resonance imaging) scans. Interpreting the results of these tests can be challenging and may lead to false detections, but the new automated system evaluates CT scans with a higher level of accuracy.
“Recent studies show that radiologists can differ in their interpretation of nodules in one patient. Automated approaches can therefore help improve the precision of lung nodule detection and serve as a preliminary interpreter to assist radiologists.
“We have developed a system that can automatically identify lung nodules of varying sizes and shapes in CT images as a tool to improve the accuracy of cancer detection.”
While other automated methods have been developed over the years, the Deakin system has proved to be more accurate.
“The experimental results demonstrate that the system performs well. Our nodule detection rate is higher than that of the existing systems, and at the same time, our false detection rate is lower than that of those systems.”
The researchers have been working on this project for a year and a half and expect the system to be available for hospital trials by early next year.