title: Helicobacter pylori-related gastric histology classification using support-vector-machine-based feature selection.
abstract: 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.
authors.affiliation: Institute of Information Science, Academia Sinica, Taipei 11523, Taiwan, ROC. nckuos@iis.sinica.edu.tw
authors.forename: Chun-Rong
authors.initials: CR
authors.lastname: Huang
authors.forename: Pau-Choo
authors.initials: PC
authors.lastname: Chung
authors.forename: Bor-Shyang
authors.initials: BS
authors.lastname: Sheu
authors.forename: Hsiu-Jui
authors.initials: HJ
authors.lastname: Kuo
authors.forename: Mikulá
authors.initials: M
authors.lastname: Popper
mesh_terms: D001185:Artificial Intelligence; D016099:Endoscopy, Gastrointestinal / Q000379:methods*; D005756:Gastritis / Q000473:pathology*; D016481:Helicobacter Infections / Q000382:microbiology / Q000473:pathology*; D016480:Helicobacter pylori / Q000302:isolation & purification*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*