Reduction of false positives in polyp detection using weighted support vector machines

Colorectal cancer is the third highest cause of cancer deaths in US (2007). Early detection and treatment of colon cancer can significantly improve patient prognosis. Manual identification of polyps by radiologists using CT Colonography can be labour intensive due to the increasing size of datasets and is error prone due to the complexity of the anatomical structures. There has been increasing interest in computer aided detection (CAD) systems for detecting polyps using CT Colonography. For a typical CAD system two major steps can be identified. In the first step image processing techniques are used to detect potential polyp candidates. Many non-polyps are inevitably found in this process. The second step attempts to discount the non-polyp candidates while maintaining true polyps. In practice this is a challenging task as training data is heavily imbalanced, that is, non-polyps dominate the data. This paper describes how the weighted support vector machine (weighted-SVM) can be used to tackle the problem effectively. The weighted-SVM generalizes the traditional SVM by applying different penalties to different classes. This trains the classifier to give favour to the most weighted class (in this case true polyps). In this paper the method was applied to data obtained from the intermediate results from a CAD system, originally applied to 209 cases. The results show that the weighted-SVM can play an important role in CAD algorithms for colorectal polyps.