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.