%0 Journal Article
%@ 21945357
%A Gardezi, S.J.S.
%A Faye, I.
%A Adjed, F.
%A Kamel, N.
%A Eltoukhy, M.M.
%D 2017
%F scholars:9416
%I Springer Verlag
%J Advances in Intelligent Systems and Computing
%K Classification (of information); Intelligent systems; Mathematical transformations; Textures, Classification accuracy; Classification performance; Curvelets; GIST; Mammogram classifications; Mass patches; Statistical features; Support vector machine classifiers, Information science
%P 705-713
%R 10.1007/978-3-319-48308-5₆₇
%T Mammogram classification using curvelet GLCM texture features and GIST features
%U https://khub.utp.edu.my/scholars/9416/
%V 533
%X This paper presents a feature fusion technique that can be used for classification of ROIs in breast cancer into normal and abnormal classes. The texture features are extracted using geometric invariant shift transform and statistical features from the curvelet grey level co-occurrence matrices. First classification accuracy of both methods were evaluated independently. Later, feature fusion is done to improve the classification performance. Support vector machine classifier with polynomial kernel was implemented using 2 � 5 folds cross validation. Fusion of features produces better results with accuracy of 92.39  as compared to 77.97  and 91  for GIST and CGLCM respectively. © Springer International Publishing AG 2017.
%Z cited By 6; Conference of 2nd International Conference on Advanced Intelligent Systems and Informatics, AISI 2016 ; Conference Date: 24 October 2016 Through 26 October 2016; Conference Code:185929