Helicobacter pylori-related gastric histology classification using support-vector-machine-based feature selection.
IEEE Trans Inf Technol Biomed
; 12(4): 523-31, 2008 Jul.
Article
em En
| MEDLINE
| ID: mdl-18632332
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Reconhecimento Automatizado de Padrão
/
Inteligência Artificial
/
Interpretação de Imagem Assistida por Computador
/
Endoscopia Gastrointestinal
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Helicobacter pylori
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Infecções por Helicobacter
/
Gastrite
Limite:
Humans
Idioma:
En
Revista:
IEEE Trans Inf Technol Biomed
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2008
Tipo de documento:
Article