A multi-classifier and decision fusion framework for robust classification of mammographic masses

Most end-to-end Computer Aided Diagnosis (CAD) systems follow a three step approach - (1) Image enhancement and segmentation, (2) Feature extraction, and, (3) Classification. While the state of the art in image enhancement and segmentation can now very accurately identify regions of interest for feature extraction, they typically result in very high dimensional feature spaces. This adversely affects the performance of classification systems because a large feature space dimensionality necessitates a large training database to accurately model the statistics of class features (e.g. benign versus malignant classes). In this work, we present a robust multi-classifier decision fusion framework that employs a divide-and-conquer approach for alleviating the affects of high dimensionality of feature vectors. The feature space is partitioned into multiple smaller sized spaces, and a bank of classifiers (a multi-classifier system) is employed to perform classification in each of the partition. Finally, a decision fusion system merges decisions from each classifier in the bank into a single decision. The system is applied to the problem of classifying digital mammographic masses as either benign or malignant.