Clustering of Medical X-ray Images by Merging Outputs of Different Classification Techniques.
Ibrahim Ziedan, Amr Ahmed Zamel, Ahmed Al Zohairy · CLEF (Working Notes) · 2015
Clustering x-ray images is a complex task, due to the great variations within each class including orientation, alignment and deformation. In this paper, an automatic medical x-ray image clustering is developed by merging the outputs from five different neural networks classifiers. Each classifier employs a set of features derived through different feature-extraction techniques. Such techniques are based on (i) pixel-value, (ii) local binary patterns, (iii) global means of rows and columns, (iv) local means of rows and columns, and (v) local histogram. A test accuracy of 86.2 % was achieved from merged output of the five NN classifiers using the ImageCLEF 2015 database. A somewhat higher accuracy of 87.2% was obtained when merging outputs of only three classifiers.