Improving Performance of Computer-Aided Detection Scheme by Combining Results from Two Machine Learning Classifiers

摘要

Rationale and ObjectivesGlobal data and local instance based machine learning methods and classifiers have been widely used to optimize computer-aided detection (CAD) schemes to classify between true-positive and false-positive detections. In this study the authors investigated the correlation between these two types of classifiers using a new independent testing dataset and assessed the potential improvement of a CAD scheme performance by combining the results of the two classifiers in detecting breast masses.