Improving the performance of machine learning algorithms using fuzzy-based features for medical x-ray image classification

This paper proposes a novel approach for medical x-ray image classification using fuzzification of Contourlet-based Center Symmetric Local Binary Patterns (CCS-LBPs). The proposed classification method consists of three stages. In the first stage, local features are obtained by partitioning each image into 25 overlapping sub-images, computing the 2-level contourlet transform of each subimage and extracting CS-LBPs from each resulting subband. In the second stage, fuzzy logic using reduced CCS-LBPs is employed to determine the degree of membership of subimages to each class. Finally, in order to assign images to their respective classes, we utilize membership values as the input of classifiers such as support vector machine (SVM) and k-nearest neighbor (K-NN). This work makes a major contribution to improve the performance of these classifiers. We conducted experiments on a subset of IRMA dataset to evaluate the effectiveness of our classification scheme. Experimental results reveal that the proposed scheme not only achieves a very good performance but also learns well even with a small number of training images.