Artif Intell Med - Computer-aided small bowel tumor detection for capsule endoscopy.

Tópicos

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Resumo

JECTIVE: Capsule endoscopy is useful in the diagnosis of small bowel diseases. However, the large number of images produced in each test is a tedious task for physicians. To relieve burden of physicians, a new computer-aided detection scheme is developed in this study, which aims to detect small bowel tumors for capsule endoscopy.METHODS AND MATERIALS: A novel textural feature based on multi-scale local binary pattern is proposed to discriminate tumor images from normal images. Since tumor in small bowel exhibit great diversities in appearance, multiple classifiers are employed to improve detection accuracy. 1200 capsule endoscopy images chosen from 10 patients' data constitute test data in our experiment.RESULTS: Multiple classifiers based on k-nearest neighbor, multilayer perceptron neural network and support vector machine, which are built from six different ensemble rules, are experimented in three different color spaces. The results demonstrate an encouraging detection accuracy of 90.50%, together with a sensitivity of 92.33% and a specificity of 88.67%.CONCLUSION: The proposed scheme using color texture features and classifier ensemble is promising for small bowel tumor detection in capsule endoscopy images.

Resumo Limpo

jectiv capsul endoscopi use diagnosi small bowel diseas howev larg number imag produc test tedious task physician reliev burden physician new computeraid detect scheme develop studi aim detect small bowel tumor capsul endoscopymethod materi novel textur featur base multiscal local binari pattern propos discrimin tumor imag normal imag sinc tumor small bowel exhibit great divers appear multipl classifi employ improv detect accuraci capsul endoscopi imag chosen patient data constitut test data experimentresult multipl classifi base knearest neighbor multilay perceptron neural network support vector machin built six differ ensembl rule experi three differ color space result demonstr encourag detect accuraci togeth sensit specif conclus propos scheme use color textur featur classifi ensembl promis small bowel tumor detect capsul endoscopi imag

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