relation: https://khub.utp.edu.my/scholars/9416/
title: Mammogram classification using curvelet GLCM texture features and GIST features
creator: Gardezi, S.J.S.
creator: Faye, I.
creator: Adjed, F.
creator: Kamel, N.
creator: Eltoukhy, M.M.
description: 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.
publisher: Springer Verlag
date: 2017
type: Article
type: PeerReviewed
identifier:   Gardezi, S.J.S. and Faye, I. and Adjed, F. and Kamel, N. and Eltoukhy, M.M.  (2017) Mammogram classification using curvelet GLCM texture features and GIST features.  Advances in Intelligent Systems and Computing, 533.  pp. 705-713.  ISSN 21945357     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994508050&doi=10.1007%2f978-3-319-48308-5_67&partnerID=40&md5=be837cf29475b0685f78e75292854546
relation: 10.1007/978-3-319-48308-5₆₇
identifier: 10.1007/978-3-319-48308-5₆₇