An efficient computer aided decision support system for breast cancer diagnosis using Echo State Network classifier

Summrina Kanwal Wajid; Amir Hussain; Bin Luo

doi:10.1109/CICARE.2014.7007829

An efficient computer aided decision support system for breast cancer diagnosis using Echo State Network classifier

Summrina Kanwal Wajid
, Amir Hussain
, Bin Luo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

The paper presents Echo State Network (ESN) as classifier to diagnose the abnormalities in mammogram images. Abnormalities in mammograms can be of different types. An efficient system which can handle these abnormalities and draw correct diagnosis is vital. We experimented with wavelet and Local Energy based Shape Histogram (LESH) features combined with Echo State Network classifier. The suggested system produces high classification accuracy of 98% as well as high sensitivity and specificity rates. We compared the performance of ESN with Support Vector Machine (SVM) and other classifiers and results generated indicate that ESN can compete with benchmark classifier and in some cases beat them. The high rate of Sensitivity and Specificity also signifies the power of ESN classifier to detect positive and negative case correctly.

Original language	English
Title of host publication	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICARE 2014
Subtitle of host publication	2014 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	17-24
Number of pages	8
ISBN (Electronic)	9781479945283
DOIs	https://doi.org/10.1109/CICARE.2014.7007829
State	Published - 12 Jan 2015
Externally published	Yes

Publication series

Name	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICARE 2014: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, Proceedings

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Computer aided decision support systems (CADSSs)
Echo state network (ESN)
Local energy based shape histogram (LESH)

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Artificial Intelligence

Access to Document

10.1109/CICARE.2014.7007829

Cite this

Wajid, S. K., Hussain, A., & Luo, B. (2015). An efficient computer aided decision support system for breast cancer diagnosis using Echo State Network classifier. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICARE 2014: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, Proceedings (pp. 17-24). Article 7007829 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICARE 2014: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CICARE.2014.7007829

Wajid, Summrina Kanwal ; Hussain, Amir ; Luo, Bin. / An efficient computer aided decision support system for breast cancer diagnosis using Echo State Network classifier. IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICARE 2014: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 17-24 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICARE 2014: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, Proceedings).

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title = "An efficient computer aided decision support system for breast cancer diagnosis using Echo State Network classifier",

abstract = "The paper presents Echo State Network (ESN) as classifier to diagnose the abnormalities in mammogram images. Abnormalities in mammograms can be of different types. An efficient system which can handle these abnormalities and draw correct diagnosis is vital. We experimented with wavelet and Local Energy based Shape Histogram (LESH) features combined with Echo State Network classifier. The suggested system produces high classification accuracy of 98\% as well as high sensitivity and specificity rates. We compared the performance of ESN with Support Vector Machine (SVM) and other classifiers and results generated indicate that ESN can compete with benchmark classifier and in some cases beat them. The high rate of Sensitivity and Specificity also signifies the power of ESN classifier to detect positive and negative case correctly.",

keywords = "Computer aided decision support systems (CADSSs), Echo state network (ESN), Local energy based shape histogram (LESH)",

author = "Wajid, \{Summrina Kanwal\} and Amir Hussain and Bin Luo",

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year = "2015",

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doi = "10.1109/CICARE.2014.7007829",

language = "English",

series = "IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICARE 2014: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, Proceedings",

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Wajid, SK, Hussain, A & Luo, B 2015, An efficient computer aided decision support system for breast cancer diagnosis using Echo State Network classifier. in IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICARE 2014: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, Proceedings., 7007829, IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICARE 2014: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 17-24. https://doi.org/10.1109/CICARE.2014.7007829

An efficient computer aided decision support system for breast cancer diagnosis using Echo State Network classifier. / Wajid, Summrina Kanwal; Hussain, Amir; Luo, Bin.
IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICARE 2014: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 17-24 7007829 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICARE 2014: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Hussain, Amir

AU - Luo, Bin

PY - 2015/1/12

Y1 - 2015/1/12

N2 - The paper presents Echo State Network (ESN) as classifier to diagnose the abnormalities in mammogram images. Abnormalities in mammograms can be of different types. An efficient system which can handle these abnormalities and draw correct diagnosis is vital. We experimented with wavelet and Local Energy based Shape Histogram (LESH) features combined with Echo State Network classifier. The suggested system produces high classification accuracy of 98% as well as high sensitivity and specificity rates. We compared the performance of ESN with Support Vector Machine (SVM) and other classifiers and results generated indicate that ESN can compete with benchmark classifier and in some cases beat them. The high rate of Sensitivity and Specificity also signifies the power of ESN classifier to detect positive and negative case correctly.

AB - The paper presents Echo State Network (ESN) as classifier to diagnose the abnormalities in mammogram images. Abnormalities in mammograms can be of different types. An efficient system which can handle these abnormalities and draw correct diagnosis is vital. We experimented with wavelet and Local Energy based Shape Histogram (LESH) features combined with Echo State Network classifier. The suggested system produces high classification accuracy of 98% as well as high sensitivity and specificity rates. We compared the performance of ESN with Support Vector Machine (SVM) and other classifiers and results generated indicate that ESN can compete with benchmark classifier and in some cases beat them. The high rate of Sensitivity and Specificity also signifies the power of ESN classifier to detect positive and negative case correctly.

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Wajid SK, Hussain A, Luo B. An efficient computer aided decision support system for breast cancer diagnosis using Echo State Network classifier. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICARE 2014: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 17-24. 7007829. (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CICARE 2014: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, Proceedings). doi: 10.1109/CICARE.2014.7007829

An efficient computer aided decision support system for breast cancer diagnosis using Echo State Network classifier

Abstract

Publication series

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this