TY  - JOUR
EP  - 713
SN  - 21945357
PB  - Springer Verlag
SP  - 705
TI  - Mammogram classification using curvelet GLCM texture features and GIST features
N1  - 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
AV  - none
VL  - 533
UR  - 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
A1  - Gardezi, S.J.S.
A1  - Faye, I.
A1  - Adjed, F.
A1  - Kamel, N.
A1  - Eltoukhy, M.M.
JF  - Advances in Intelligent Systems and Computing
Y1  - 2017///
KW  - Classification (of information); Intelligent systems; Mathematical transformations; Textures
KW  -  Classification accuracy; Classification performance; Curvelets; GIST; Mammogram classifications; Mass patches; Statistical features; Support vector machine classifiers
KW  -  Information science
ID  - scholars9416
N2  - 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.
ER  -