research-article

Effective tumor feature extraction for smart phone based microwave tomography breast cancer screening

Authors:

Samaneh Aminikhanghahi,

Wei Wang,

Sung Shin,

Seong H. Son,

Soon I. JeonAuthors Info & Claims

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

Pages 674 - 679

https://doi.org/10.1145/2554850.2554936

Published: 24 March 2014 Publication History

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Abstract

Mobile Microwave Tomography (MMT) is a new alternative technique to detect breast cancer using smart phone based electronic healthcare system. In this paper, we propose a new solution to extract tumor information from MMT raw data for early breast cancer screening. MMT reflects water contents of breast tissue by measuring their electrical properties and sends permittivity and conductivity raw data to processing servers in hospital via WiFi or 3G/4G networks. In this approach we investigate three different sets of MMT tumor features and perform a comparative study to investigate their set of accuracy measurements for each classification. Through extensive empirical study of the classification results, we have identified the following six parameters as useful to extract tumor information: average permittivity of healthy tissue (APHT), average permittivity of probable tumor area (APPTA), maximum and minimum values of permittivity of probable tumor area (MaxPPTA, and MinPPTA), and energy values of healthy tissue (EVHT) and probable tumor area (EVPA).

References

[1]

Breastcancer.org. (2013, Aug 10). U.S. Breast Cancer Statistics. Available: http://www.cancer.org/cancer/breastcancer/detailedguide/breast-cancer-key-statistics

Google Scholar

[2]

Elkan, C. Nearest Neighbor Classification. http://cseweb.ucsd.edu/~elkan/250B/nearestn.pdf12

Google Scholar

[3]

Fear, E. C., Meaney, P. M. and Stuchly, M. A. 2003. Microwave for breast cancer detection. IEEE Potentials, 22, 1 (2003), 12--18.

Crossref

Google Scholar

[4]

Koller, D. and Sahami, M., Toward optimal feature selection. Proceedings of International Conference on Machine Learning, 1996.

Google Scholar

[5]

Kraipeerapun, P. and Fung, C. C. Binary classification using ensemble neural networks and interval neutrosophic sets. Journal of Neurocomputing, 72(2009)2845--2856.

Digital Library

Google Scholar

[6]

Marcano-Cede, A., Quintanilla-Domnguez, J. and Andina, D. WBCD breast cancer database classification applying artificial metaplasticity neural network. Journal of Expert Systems with Applications, 38 (2011), 9573--9579.

Digital Library

Google Scholar

[7]

Moon, J., Kim, H. I., Choi, H. D. and Jeon, S. I. A Support Vector Machine based Classifier to Extract Abnormal Features from Breast Magnetic Resonance Images. Proceedings of the 2012 ACM Research in Applied Computation Symposium. ACM, 2012.

Digital Library

Google Scholar

[8]

Noghanian, S. Microwave Tomography for Biomedical Quantitative Imaging. Journal of electrical & electronics, 1, 3 (2012).

Google Scholar

[9]

Ozyildirim, B. M. and Avci, M. Generalized classifier neural network. Journal of neural networks, 39 (2013), 18--26.

Digital Library

Google Scholar

[10]

Sapna1, S., Tamilarasi, A. and Pravin Kumar, M. Backpropagation Learning Algorithm Based On Levenberg Marquardt Algorithm. DOI=

Crossref

Google Scholar

[11]

Saritas, I. Prediction of Breast Cancer Using Artificial Neural Networks. Journal of medical system, 36 (2012), 2901--2907.

Digital Library

Google Scholar

[12]

Shekhar Singh, P. R. Gupta. Breast Cancer detection and Classification using Neural Network. International Journal Of Advanced Engineering Sciences And Technologies, 6, 1 (2011), 004--009.

Google Scholar

[13]

Son, S., Simonov, N., Kim, H., Lee, J., and Jeon, S. Preclinical Prototype Development of a Microwave Tomography System for Breast Cancer Detection. ETRI Journal 32 (2010).

Google Scholar

[14]

Wadhwani, S., Wadhwani, A. K. and Saraswat, M. Classification of Breast Cancer Detection Using Artificial Neural Networks. Engineering, Science and Technology (CREST) Journals, 1, 3 (2013), 85--91.

Google Scholar

Cited By

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Pack CSon SShin S(2017)Computer aided diagnosis with boosted learning for anomaly detection in microwave tomographyACM SIGAPP Applied Computing Review10.1145/3161534.316153817:3(39-47)Online publication date: 14-Nov-2017
https://dl.acm.org/doi/10.1145/3161534.3161538
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
Pack CShin SChoi HJeon SKim JOssowski S(2016)Optimized multilayer perceptron using dynamic learning rate based microwave tomography breast cancer screeningProceedings of the 31st Annual ACM Symposium on Applied Computing10.1145/2851613.2851825(2171-2175)Online publication date: 4-Apr-2016
https://dl.acm.org/doi/10.1145/2851613.2851825
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Index Terms

Effective tumor feature extraction for smart phone based microwave tomography breast cancer screening
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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Information & Contributors

Information

Published In

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

March 2014

1890 pages

ISBN:9781450324694

DOI:10.1145/2554850

Conference Chairs:
Yookun Cho
Seoul National University, Korea
,
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Sangwook Kim
Kyungpook National University, Korea
,
Chih-Cheng Hung
Southern Polytechnic State University
,
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: 24 March 2014

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Ministry of Science, ICT and Future Planning

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SAC 2014

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SIGAPP

SAC 2014: Symposium on Applied Computing

March 24 - 28, 2014

Gyeongju, Republic of Korea

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SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;

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

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Cited By

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Pack CSon SShin S(2017)Computer aided diagnosis with boosted learning for anomaly detection in microwave tomographyACM SIGAPP Applied Computing Review10.1145/3161534.316153817:3(39-47)Online publication date: 14-Nov-2017
https://dl.acm.org/doi/10.1145/3161534.3161538
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
Pack CShin SChoi HJeon SKim JOssowski S(2016)Optimized multilayer perceptron using dynamic learning rate based microwave tomography breast cancer screeningProceedings of the 31st Annual ACM Symposium on Applied Computing10.1145/2851613.2851825(2171-2175)Online publication date: 4-Apr-2016
https://dl.acm.org/doi/10.1145/2851613.2851825
Guo HHuang HWang JTang SZhao ZSun ZSun YHuang LLiu H(2016)Tefnut: An Accurate Smartphone Based Rain Detection System in VehiclesWireless Algorithms, Systems, and Applications10.1007/978-3-319-42836-9_2(13-23)Online publication date: 4-Aug-2016
https://doi.org/10.1007/978-3-319-42836-9_2
Aminikhanghahi SShin SWang WJeon SSon SPack CWainwright RCorchado JBechini AHong J(2015)Study of wireless mammography image transmission impacts on robust cyber-aided diagnosis systemsProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2695832(2252-2256)Online publication date: 13-Apr-2015
https://dl.acm.org/doi/10.1145/2695664.2695832
Aminikhanghahi SShin SWang WSon SJeon SLu CNadimi EKim SWang W(2014)An optimized support vector machine classifier to extract abnormal features from breast microwave tomography dataProceedings of the 2014 Conference on Research in Adaptive and Convergent Systems10.1145/2663761.2664230(111-115)Online publication date: 5-Oct-2014
https://dl.acm.org/doi/10.1145/2663761.2664230
Wang CWang WShin SJeon SLu CNadimi EKim SWang W(2014)Comparative study of microwave tomography segmentation techniques based on GMM and KNN in breast cancer detectionProceedings of the 2014 Conference on Research in Adaptive and Convergent Systems10.1145/2663761.2663769(303-308)Online publication date: 5-Oct-2014
https://dl.acm.org/doi/10.1145/2663761.2663769

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