Enhancing automatic polyp detection accuracy using fusion techniques

Alaa El-Khatib, Naoufel Werghi, Hussain Al-Ahmad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

In this paper, we address the problem of automatic polyp detection in endoscopy videos. We propose feature fusion and multiple classifier technique using a variety of features that include wavelet features, Local Binary Patterns and Gabor features. We show that such a combination can mitigate, to a reasonable extent, the high rate of false positives common in this particular problem. Moreover, we study the effect of specular reflection removal on performance and show that, despite being a common preprocessing step, it can in some cases lead to worse results. Experiments conducted with ASU-Mayo Clinic Polyp Database confirm the validity of our scheme.

Original languageBritish English
Title of host publication2016 IEEE 59th International Midwest Symposium on Circuits and Systems, MWSCAS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509009169
DOIs
StatePublished - 2 Mar 2017
Event59th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2016 - Abu Dhabi, United Arab Emirates
Duration: 16 Oct 201619 Oct 2016

Publication series

NameMidwest Symposium on Circuits and Systems
ISSN (Print)1548-3746

Conference

Conference59th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2016
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period16/10/1619/10/16

Fingerprint

Dive into the research topics of 'Enhancing automatic polyp detection accuracy using fusion techniques'. Together they form a unique fingerprint.

Cite this