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

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 foridentifying and classifying wireless capsule endoscopic images, and investigate statistical measures to differentiatenormal and abnormal images. The proposed technique consists of two main stages, namely, feature extraction andclassification. Primarily, 32 features incorporating four statistical measures （contrast, correlation, homogeneity andenergy） calculated from co-occurrence metrics were computed. Then, mutual information was used to select featureswith maximal dependence on the target class and with minimal redundancy between features. Finally, a trainedclassifier, adaptive neuro-fuzzy interface system was implemented to classify endoscopic images into tumor, healthyand unhealthy classes. Classification accuracy of 94.2% was obtained using the proposed pipeline. Such techniquesare valuable for accurate detection characterization and interpretation of endoscopic images.