Journal
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
Volume 12, Issue 4, Pages 523-531Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITB.2007.913128
Keywords
endoscopy; feature selection; Helicobacter pylori (H. pylori); nonulcer dyspepsia; peptic ulcer; support vector machine (SVM)
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This study presents a computer-aided diagnosis system using sequential forward floating selection (SFFS) with support vector machine (SVM) to diagnose gastric histology of Heliobacter pylori (H. pylori) from endoscopic images. To achieve this goal, candidate image features associated with clinical symptoms ire 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.
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