article

Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural

Authors:

Qutaishat Munib,

Abdallah MoussaAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 28, Issue 4

Pages 713 - 723

https://doi.org/10.1016/j.eswa.2004.12.028

Published: 01 May 2005 Publication History

Abstract

The high incidence of breast cancer in women has increased significantly in the recent years. The most familiar breast tumors types are mass and microcalcification. Mammograms-breast X-ray-are considered the most reliable method in early detection of breast cancer. Computer-aided diagnosis system can be very helpful for radiologist in detection and diagnosing abnormalities earlier and faster than traditional screening programs. Several techniques can be used to accomplish this task. In this paper, two techniques are proposed based on wavelet analysis and fuzzy-neural approaches. These techniques are mammography classifier based on globally processed image and mammography classifier based on locally processed image (region of interest). The system is classified normal from abnormal, mass for microcalcification and abnormal severity (benign or malignant). The evaluation of the system is carried out on Mammography Image Analysis Society (MIAS) dataset. The accuracy achieved is satisfied.

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Cited By

Bakkouri IAfdel K(2019)Multi-scale CNN based on region proposals for efficient breast abnormality recognitionMultimedia Tools and Applications10.1007/s11042-018-6267-z78:10(12939-12960)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1007/s11042-018-6267-z
Jasmine JJoans S(2017)Robust automatic classification of benign and malignant microcalcification and mass in digital mammographyInternational Journal of Business Intelligence and Data Mining10.1504/IJBIDM.2016.08186911:3(282-298)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1504/IJBIDM.2016.081869
Raghavendra URajendra Acharya UFujita HGudigar ATan JChokkadi S(2016)Application of Gabor wavelet and Locality Sensitive Discriminant Analysis for automated identification of breast cancer using digitized mammogram imagesApplied Soft Computing10.1016/j.asoc.2016.04.03646:C(151-161)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.04.036
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Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural
1. Computing methodologies
  1. Computer graphics
  2. Machine learning

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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 28, Issue 4

May, 2005

231 pages

ISSN:0957-4174

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2005.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 May 2005

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Cited By

Bakkouri IAfdel K(2019)Multi-scale CNN based on region proposals for efficient breast abnormality recognitionMultimedia Tools and Applications10.1007/s11042-018-6267-z78:10(12939-12960)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1007/s11042-018-6267-z
Jasmine JJoans S(2017)Robust automatic classification of benign and malignant microcalcification and mass in digital mammographyInternational Journal of Business Intelligence and Data Mining10.1504/IJBIDM.2016.08186911:3(282-298)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1504/IJBIDM.2016.081869
Raghavendra URajendra Acharya UFujita HGudigar ATan JChokkadi S(2016)Application of Gabor wavelet and Locality Sensitive Discriminant Analysis for automated identification of breast cancer using digitized mammogram imagesApplied Soft Computing10.1016/j.asoc.2016.04.03646:C(151-161)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.04.036
Gedik N(2016)A new feature extraction method based on multi-resolution representations of mammogramsApplied Soft Computing10.1016/j.asoc.2016.04.00444:C(128-133)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.04.004
Wajid SHussain A(2015)Local energy-based shape histogram feature extraction technique for breast cancer diagnosisExpert Systems with Applications: An International Journal10.1016/j.eswa.2015.04.05742:20(6990-6999)Online publication date: 15-Nov-2015
https://dl.acm.org/doi/10.1016/j.eswa.2015.04.057
Joo TKim S(2015)Time series forecasting based on wavelet filteringExpert Systems with Applications: An International Journal10.1016/j.eswa.2015.01.02642:8(3868-3874)Online publication date: 15-May-2015
https://dl.acm.org/doi/10.1016/j.eswa.2015.01.026
Pak FKanan HAlikhassi A(2015)Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super ResolutionComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2015.06.009122:2(89-107)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1016/j.cmpb.2015.06.009
Meng TSoliman AShyu MYang YChen SIyengar SYordy JIyengar P(2013)Wavelet Analysis in Current Cancer Genome ResearchIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2013.13410:6(1442-14359)Online publication date: 1-Nov-2013
https://dl.acm.org/doi/10.1109/TCBB.2013.134
Malar EKandaswamy AGauthaam M(2013)Multiscale and Multilevel Wavelet Analysis of Mammogram Using Complex Neural NetworkProceedings of the 4th International Conference on Swarm, Evolutionary, and Memetic Computing - Volume 829810.1007/978-3-319-03756-1_59(658-668)Online publication date: 19-Dec-2013
https://dl.acm.org/doi/10.1007/978-3-319-03756-1_59
Lai YChung KChen CLin GWang C(2012)Novel mean-shift based histogram equalization using textured regionsExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.08.13439:3(2750-2758)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1016/j.eswa.2011.08.134
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