research-article

SVM-based Harris corner detection for breast mammogram image normal/abnormal classification

Authors:

Soon I. JeonAuthors Info & Claims

RACS '13: Proceedings of the 2013 Research in Adaptive and Convergent Systems

Pages 187 - 191

https://doi.org/10.1145/2513228.2513324

Published: 01 October 2013 Publication History

Abstract

The breast mammogram image is one of the most important materials of the Computer-Aided Diagnosis (CAD) system to support diagnosis of breast cancer. In the CAD system, intensity value is a widely used feature for medical image processing. In this paper, we propose develop improved Harris Corner Detection with improved input training data set for Support Vector Machine (SVM) to classify a breast mammogram image as normal or abnormal. In the proposed approach, corner pixels from improved Harris Corner Detection are used as a training input feature for SVM. The results demonstrate that the proposed approach can significantly improve both the accuracy and the performance of computational speed to classify the breast mammogram image as normal or abnormal, when compared with the data set from traditional methods.

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Cited By

Khan SGulzar YTuraev SPeng Y(2021)A Modified HSIFT Descriptor for Medical Image Classification of Anatomy ObjectsSymmetry10.3390/sym1311198713:11(1987)Online publication date: 20-Oct-2021
https://doi.org/10.3390/sym13111987
Herur SKerur SHadimani H(2020)Machine Learning-Based Implementation of Image Corner Detection Using SVM Algorithm for Biomedical ApplicationsNanoelectronics, Circuits and Communication Systems10.1007/978-981-15-7486-3_19(193-202)Online publication date: 18-Nov-2020
https://doi.org/10.1007/978-981-15-7486-3_19
Taheri MHamer GSon SShin S(2016)Enhanced Breast Cancer Classification with Automatic Thresholding Using SVM and Harris Corner DetectionProceedings of the International Conference on Research in Adaptive and Convergent Systems10.1145/2987386.2987420(56-60)Online publication date: 11-Oct-2016
https://dl.acm.org/doi/10.1145/2987386.2987420
Show More Cited By

Index Terms

SVM-based Harris corner detection for breast mammogram image normal/abnormal classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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Published In

cover image ACM Conferences

RACS '13: Proceedings of the 2013 Research in Adaptive and Convergent Systems

October 2013

529 pages

ISBN:9781450323482

DOI:10.1145/2513228

Conference Chairs:
Ching Y. Suen
Concordia University, Canada
,
Amir Aghdam
Concordia University, Canada
,
Minyi Guo
Shanghai Jiao Tong University, China
,
Program Chairs:
Jiman Hong
Soongsil University, Korea
,
Esmaeil Nadimi
University of Southern Denmark, Denmark

Copyright © 2013 ACM.

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SIGAPP: ACM Special Interest Group on Applied Computing
ACCT: Association of Convergent Computing Technology

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2013

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Ministry of Science, ICT and Future Planning

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RACS'13

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SIGAPP
ACCT

RACS'13: Research in Adaptive and Convergent Systems

October 1 - 4, 2013

Quebec, Montreal, Canada

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RACS '13 Paper Acceptance Rate 73 of 317 submissions, 23%;

Overall Acceptance Rate 393 of 1,581 submissions, 25%

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RACS '24

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International Conference on Research in Adaptive and Convergent Systems

November 5 - 8, 2024

Pompei , Italy

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Cited By

Khan SGulzar YTuraev SPeng Y(2021)A Modified HSIFT Descriptor for Medical Image Classification of Anatomy ObjectsSymmetry10.3390/sym1311198713:11(1987)Online publication date: 20-Oct-2021
https://doi.org/10.3390/sym13111987
Herur SKerur SHadimani H(2020)Machine Learning-Based Implementation of Image Corner Detection Using SVM Algorithm for Biomedical ApplicationsNanoelectronics, Circuits and Communication Systems10.1007/978-981-15-7486-3_19(193-202)Online publication date: 18-Nov-2020
https://doi.org/10.1007/978-981-15-7486-3_19
Taheri MHamer GSon SShin S(2016)Enhanced Breast Cancer Classification with Automatic Thresholding Using SVM and Harris Corner DetectionProceedings of the International Conference on Research in Adaptive and Convergent Systems10.1145/2987386.2987420(56-60)Online publication date: 11-Oct-2016
https://dl.acm.org/doi/10.1145/2987386.2987420
Khan SYong SDeng J(2015)Ensemble classification with modified SIFT descriptor for medical image modality2015 International Conference on Image and Vision Computing New Zealand (IVCNZ)10.1109/IVCNZ.2015.7761517(1-6)Online publication date: Nov-2015
https://doi.org/10.1109/IVCNZ.2015.7761517
Khan SYong S(2015)Analysis of primitive features for medical image modality classification2015 International Symposium on Mathematical Sciences and Computing Research (iSMSC)10.1109/ISMSC.2015.7594028(60-65)Online publication date: May-2015
https://doi.org/10.1109/ISMSC.2015.7594028

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