A Learning Method for Automated Polyp Detection
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
A Comparative Study of Texture Features for the Discrimination of Gastric Polyps in Endoscopic Video
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Colon cancer is one of the most common cancers in developed countries. Most of these cancers start with a polyp. Polyps are easily detected by physicians. Our goal is to mimic this detection ability so that endoscopic videos can be pre-scanned with our algorithm before the physician analyses them. The method will indicate which part of the video needs attention (polyps were detected there) and hence can speedup the procedures. In this paper we present a method for polyp detection in endoscopic images that uses SVM for classification. Our experiments yielded a result of 93.16 ± 0.09% of area under the Receiver Operating Characteristic (ROC) curve on a database of 4620 images indicating that the approach proposed is well suited to the detection of polyps in endoscopic video.