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Google-Like Model Used To Diagnose Suspicious Breast Masses Quickly

imilar enough to a known case of breast cancer, then this would suggest the presence of cancer.

Although diagnostically accurate, this practice becomes inefficient as the image database increases in size. Therefore, the researchers incorporate an additional approach.

Instead of comparing the new unknown case with all mammography images stored in the knowledge database, the researchers restrict the analysis to the stored cases that are most informative. The selection of the most informative cases is done using an image indexing strategy based on the concept of "image entropy." Image entropy represents a measure of the disorder or complexity in the image. An image that is all black or white has zero entropy. An image of a checkerboard has low entropy—it consists of an equal number of light and dark pixels. Complex images with more uniform distributions of many pixel intensity levels have higher entropy and are considered more informative in the context of the Duke system.

Normal breast tissue "can be as complex as a tumor," Tourassi says. "This is precisely the reason mammographic diagnosis is such a challenging task. Our database inlcudes normal cases as well in the decision-making process."

In the recent pilot study, the Duke researchers applied their technique to a database of 2,300 mammography images. With entropy indexing, the researchers compared a sample image to the top 600 most informative, cutting down their CAD system's processing time by one-fourth, to less than 3 seconds per query. The researchers expect to launch a larger study in a year to evaluate the clinical impact of this new approach.

Source: Eurekalert
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