Detection of breast thermograms using ensemble classifiers

Mortality rate of breast cancer can be reduced bydetecting breast cancer in its early stage. Breast thermographyplays an important role in early detection of breast cancer, as itcan detect tumors when the physiological changes start in thebreast prior to structural changes. Computer Aided Detection(CAD) systems improve the diagnostic accuracy by providing adetailed analysis of images, which are not visible to the nakedeye. The performance of CAD systems depends on many factors.One of the important factors is the classifier used forclassification of breast thermograms. In this paper, we made acomparison of classifier performances using two ensembleclassifiers namely Ensemble Bagged Trees and AdaBoost.Spatial and spectral features are used for classification.Ensemble Bagged Trees classifier performed better thanAdaBoost in terms of accuracy of classification, but trainingtime required is higher than AdaBoost classifier. An accuracy of87%, sensitivity of 83% and specificity of 90.6% is obtainedusing Ensemble Bagged Trees classifier.