Helicobacter Pylori-related gastric histology classification using support-vector-machine-based feature selection

Chun Rong Huang, Pau Choo Chung, Bor Shyang Sheu, Hsiu Jui Kuo, Mikuláš Popper

研究成果: Article同行評審

28 引文 斯高帕斯(Scopus)

摘要

This study presents a computer-aided diagnosis system using sequential forward floating selection (SFFS) with support vector machine (SVM) to diagnose gastric histology of Helicobacter pylori H. pylori) from endoscopic images. To achieve this goal, candidate image features associated with clinical symptoms are extracted from endoscopic images. With these candidate features, the SFFS method is applied to select feature subsets, which perform the best classification results under SVM with respect to different histological features. By using the classifiers obtained from the feature subsets, a new diagnosis system is implemented to provide physicians with H. pylori -related histological results from endoscopic images.

原文English
頁(從 - 到)523-531
頁數9
期刊IEEE Transactions on Information Technology in Biomedicine
12
發行號4
DOIs
出版狀態Published - 2008 7月

All Science Journal Classification (ASJC) codes

  • 生物技術
  • 電腦科學應用
  • 電氣與電子工程

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