Paper Info
Title
Improving the performance of machine learning algorithms using fuzzy-based features for medical x-ray image classification
Abstract
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.
Year
DOI
Venue
2014
10.3233/IFS-141273
Journal of Intelligent and Fuzzy Systems
Keywords
Field
DocType
medical x-ray images,contourlet transform,image classification,local binary patterns,fuzzy membership
Small number,Data mining,Fuzzy classification,Local binary patterns,Fuzzy set,Artificial intelligence,Contextual image classification,Contourlet,Pattern recognition,Fuzzy logic,Support vector machine,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
27
6
1064-1246
Citations 
PageRank 
References 
0
0.34
10
Authors
4
Name
Order
Citations
PageRank
Fatemeh Ghofrani110.69
Mohammad Sadegh Helfroush27011.30
Habibollah Danyali34911.07
Kamran Kazemi48112.24