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Further Investigations for Documents Information Retrieval Based on DWT Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Mammogram Classification Using Curvelet GLCM Texture Features and GIST Features

Authors : Syed Jamal Safdar Gardezi, Ibrahima Faye, Faouzi Adjed, Nidal Kamel, Mohamed Meselhy Eltoukhy

Published in: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016

Publisher: Springer International Publishing

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Abstract

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.

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Metadata
Title
Mammogram Classification Using Curvelet GLCM Texture Features and GIST Features
Authors
Syed Jamal Safdar Gardezi
Ibrahima Faye
Faouzi Adjed
Nidal Kamel
Mohamed Meselhy Eltoukhy
Copyright Year
2017
Book
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016

Print ISBN: 978-3-319-48307-8

Electronic ISBN: 978-3-319-48308-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-48308-5_67

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