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Study of wireless mammography image transmission impacts on robust cyber-aided diagnosis systems

Authors:

Samaneh Aminikhanghahi,

Chulwoo PackAuthors Info & Claims

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

Pages 2252 - 2256

https://doi.org/10.1145/2695664.2695832

Published: 13 April 2015 Publication History

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Abstract

Wireless cyber-mammography is potentially a convenient screening method to be comfortable and effective in community and rural area early detection of breast cancer, but their interpretation is difficult due to the noise and low quality of images. In this paper, we study the accuracy of a Cyber-aided diagnosis system to help physicians to classify the detected regions in wireless mammogram images into malignant or benign categories. In this approach we investigate different sets of features and two classifier methods (SVM and GMM) and perform a comparative study to investigate the accuracy measurements in noisy condition. The results show that without any noise or errors, SVM classifier outperforms GMM; however GMM classifier is more robust and reliable in noisy circumstance especially in detecting malignant cases. The proposed study provides in-depth understanding of the accuracy and reliability of wireless mammography in early breast cancer detection.

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Cited By

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Gupta TGupta PPal VBhatia K(2022)Breast Cancer Detection Using Different Classification and Ensemble MethodsSmart Healthcare for Sustainable Urban Development10.4018/978-1-6684-2508-4.ch003(28-50)Online publication date: 24-Jun-2022
https://doi.org/10.4018/978-1-6684-2508-4.ch003
Amethiya YPipariya PPatel SShah M(2021)Comparative Analysis of Breast Cancer detection using Machine Learning and BiosensorsIntelligent Medicine10.1016/j.imed.2021.08.004Online publication date: Oct-2021
https://doi.org/10.1016/j.imed.2021.08.004
Pack CShin SSon SJeon SNadimi ECerny TKim SWang W(2015)Computer aided breast cancer diagnosis system with fuzzy multiple-parameter support vector machineProceedings of the 2015 Conference on research in adaptive and convergent systems10.1145/2811411.2811504(172-176)Online publication date: 9-Oct-2015
https://dl.acm.org/doi/10.1145/2811411.2811504

Index Terms

Study of wireless mammography image transmission impacts on robust cyber-aided diagnosis systems
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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Information

Published In

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

April 2015

2418 pages

ISBN:9781450331968

DOI:10.1145/2695664

Conference Chairs:
Roger L. Wainwright
University of Tulsa
,
Juan Manuel Corchado
University of Salamanca, Spain
,
Program Chairs:
Alessio Bechini
University of Pisa, Italy
,
Jiman Hong
Soongsil University, South Korea

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2015

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Funding Sources

MSIP (Ministry of Science, ICT & Future Planning), Korea

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SAC 2015

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SIGAPP

SAC 2015: Symposium on Applied Computing

April 13 - 17, 2015

Salamanca, Spain

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SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all

Gupta TGupta PPal VBhatia K(2022)Breast Cancer Detection Using Different Classification and Ensemble MethodsSmart Healthcare for Sustainable Urban Development10.4018/978-1-6684-2508-4.ch003(28-50)Online publication date: 24-Jun-2022
https://doi.org/10.4018/978-1-6684-2508-4.ch003
Amethiya YPipariya PPatel SShah M(2021)Comparative Analysis of Breast Cancer detection using Machine Learning and BiosensorsIntelligent Medicine10.1016/j.imed.2021.08.004Online publication date: Oct-2021
https://doi.org/10.1016/j.imed.2021.08.004
Pack CShin SSon SJeon SNadimi ECerny TKim SWang W(2015)Computer aided breast cancer diagnosis system with fuzzy multiple-parameter support vector machineProceedings of the 2015 Conference on research in adaptive and convergent systems10.1145/2811411.2811504(172-176)Online publication date: 9-Oct-2015
https://dl.acm.org/doi/10.1145/2811411.2811504

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Computer-aided detection and diagnosis of breast cancer with mammography: recent advances

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