An integrated computer-aided diagnosis (CAD) system developed by researchers from Kyung Hee University and Sungkyunkwan University in South Korea could outperform current conventional deep learning methodologies used by radiologists to detect, segment and classify tumors from digital x-ray mammograms.

Classifying masses as benign or malignant can be a time-consuming process for radiologists. A supplemental reading done by another expert, in this case one that’s computerized, may increase overall accuracy and specificity while reducing false positives and negatives, wrote lead author Mugahed Al-antari, PhD, a professor of biomedical engineering at Kyung Hee University, and colleagues in research published in the August issue of the International Journal of Medical Informatics.

The researchers’ CAD system comprises three main deep learning components to detect, segment and classify a breast mass from an entire mammogram.