Comparing one-class and two-class SVM classifiers for normal mammogram detection

X-ray mammograms are one of the most common techniques used by radiologists for breast cancer detection and diagnosis. Early detection is important, which raised the importance of developing Computer-Aided Detection and Diag-nosis(CAD) systems. Although most(CAD)systems were designed to help radiologists in their diagnosis by providing useful insight, the accuracy of CAD systems remains below the level that would lead to an improvement in the overall radiologists' performance. Unlike other CAD systems who aim to detect abnormal mammograms, we are designing a pre-CAD system that aims to detect normal mammograms instead of abnormal ones. The pre-CAD system works as a "first look" and screens-out normal mammograms, leaving the radiologists and other conventional CAD systems to focus on the suspicious cases. Support Vector Machine classifiers are used to detect normal mammograms. We are comparing the effect of using 1-class and 2-class SVMs when normal mammogram, instead of abnormal, is detected. Results showed that our pre-CAD system performance for 1-class outperformed 2-class SVM classifiers almost always. Using our set of features, 1-class SVM achieved a specificity of (99.2%), while the two-class SVM achieved (86.71%) respectively.