Tumor CE image classification using SVM-based feature selection
Refereed conference paper presented and published in conference proceedings

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AbstractIn this paper, we propose a new scheme aimed for gastrointestinal (GI) tumor capsule endoscopy (CE) images classification, which utilizes sequential forward floating selection (SFFS) together with support vector machine (SVM). To achieve this goal, candidate features related to texture characteristics of CE images are extracted. With these candidate features, SFFS based on SVM is applied to select the most discriminative features that can separate normal CE images from tumor CE images. Comprehensive experiments on our present CE image data verify that it is promising to employ the proposed scheme to recognize tumor CE images. ©2010 IEEE.
All Author(s) ListLi B., Meng M.Q.-H.
Name of Conference23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010
Start Date of Conference18/10/2010
End Date of Conference22/10/2010
Place of ConferenceTaipei
Country/Region of ConferenceTaiwan
Detailed descriptionorganized by IEEE/RSJ ,
Pages1322 - 1327
LanguagesEnglish-United Kingdom

Last updated on 2020-22-11 at 00:37