Computer-aided diagnosis usually screens thousands of instances to find only a few positive cases that indicate
probable presence of disease.The amount of patient data increases consistently all the time. In diagnosis of new
instances, disagreement occurs between a CAD system and physicians, which suggests inaccurate classifiers.
Intuitively, misclassified instances and the previously acquired data should be used to retrain the classifier.
This, however, is very time consuming and, in some cases where dataset is too large, becomes infeasible. In
addition, among the patient data, only a small percentile shows positive sign, which is known as imbalanced
data.We present an incremental Support Vector Machines(SVM) as a solution for the class imbalance problem
in classification of anomaly in medical images. The support vectors provide a concise representation of the
distribution of the training data. Here we use bootstrapping to identify potential candidate support vectors
for future iterations. Experiments were conducted using images from endoscopy videos, and the sensitivity
and specificity were close to that of SVM trained using all samples available at a given incremental step with
significantly improved efficiency in training the classifier.