Detection of small bowel tumor in wireless capsule endoscopy images using an adaptive neuro-fuzzy inference system

Mahdi Alizadeh, Omid Haji Maghsoudi, Kaveh Sharzehi, Hamid Reza Hemati, Alireza Kamali Asl, Alireza Talebpour

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Automatic diagnosis tool helps physicians to evaluate capsule endoscopic examinations faster and more accurate. The purpose of this study was to evaluate the validity and reliability of an automatic post-processing method for identifying and classifying wireless capsule endoscopic images, and investigate statistical measures to differentiate normal and abnormal images. The proposed technique consists of two main stages, namely, feature extraction and classification. Primarily, 32 features incorporating four statistical measures (contrast, correlation, homogeneity and energy) calculated from co-occurrence metrics were computed. Then, mutual information was used to select features with maximal dependence on the target class and with minimal redundancy between features. Finally, a trained classifier, adaptive neuro-fuzzy interface system was implemented to classify endoscopic images into tumor, healthy and unhealthy classes. Classification accuracy of 94.2% was obtained using the proposed pipeline. Such techniques are valuable for accurate detection characterization and interpretation of endoscopic images.

Original languageEnglish (US)
Pages (from-to)419-427
Number of pages9
JournalJournal of Biomedical Research
Issue number5
StatePublished - 2017
Externally publishedYes


  • Adaptive neuro-fuzzy inference system
  • Co-occurrence matrix
  • Texture feature
  • Wireless capsule endoscopy

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)


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