Tumor CE Image Classification Using SVM-Based Feature Selection

被引：0
|
作者：
Li, Baopu [1 ]
Meng, Max Q-H [1 ]
机构：
[1] Chinese Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China
来源：
IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010) | 2010年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In 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.
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页数：6
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