Evaluation of breast cancer tumor classification with unconstrained functional networks classifier

Emad A. El-Sebakhy; Kanaan Abed Faisal; T. Helmy; F. Azzedin; A. Al-Suhaim

doi:10.1109/aiccsa.2006.205102

Evaluation of breast cancer tumor classification with unconstrained functional networks classifier

Emad A. El-Sebakhy^*
, Kanaan Abed Faisal
, T. Helmy
, F. Azzedin
, A. Al-Suhaim

^*Corresponding author for this work

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

23 Scopus citations

Abstract

This paper proposes functional networks as an unconstrained classifier scheme for multivariate data to diagnose the breast cancer tumor. The performance of this new technique is measured using two well known databases under the minimum description length criterion, the results are compared with the most common existing classifiers in both computer science and statistics literatures. This new classifier shown reliable and efficient results with better correct classification rate, and much less computational time.

Original language	English
Title of host publication	IEEE International Conference on Computer Systems and Applications, 2006
Publisher	IEEE Computer Society
Pages	281-287
Number of pages	7
ISBN (Print)	1424402123, 9781424402120
DOIs	https://doi.org/10.1109/aiccsa.2006.205102
State	Published - 2006

Publication series

Name	IEEE International Conference on Computer Systems and Applications, 2006
Volume	2006

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Breast cancer detection
Functional networks
Minimum description length
Pattern classification

ASJC Scopus subject areas

General Engineering

Access to Document

10.1109/aiccsa.2006.205102

Cite this

El-Sebakhy, E. A., Faisal, K. A., Helmy, T., Azzedin, F., & Al-Suhaim, A. (2006). Evaluation of breast cancer tumor classification with unconstrained functional networks classifier. In IEEE International Conference on Computer Systems and Applications, 2006 (pp. 281-287). Article 1618367 (IEEE International Conference on Computer Systems and Applications, 2006; Vol. 2006). IEEE Computer Society. https://doi.org/10.1109/aiccsa.2006.205102

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title = "Evaluation of breast cancer tumor classification with unconstrained functional networks classifier",

abstract = "This paper proposes functional networks as an unconstrained classifier scheme for multivariate data to diagnose the breast cancer tumor. The performance of this new technique is measured using two well known databases under the minimum description length criterion, the results are compared with the most common existing classifiers in both computer science and statistics literatures. This new classifier shown reliable and efficient results with better correct classification rate, and much less computational time.",

keywords = "Breast cancer detection, Functional networks, Minimum description length, Pattern classification",

author = "El-Sebakhy, \{Emad A.\} and Faisal, \{Kanaan Abed\} and T. Helmy and F. Azzedin and A. Al-Suhaim",

year = "2006",

doi = "10.1109/aiccsa.2006.205102",

language = "English",

isbn = "1424402123",

series = "IEEE International Conference on Computer Systems and Applications, 2006",

publisher = "IEEE Computer Society",

pages = "281--287",

booktitle = "IEEE International Conference on Computer Systems and Applications, 2006",

address = "United States",

}

El-Sebakhy, EA, Faisal, KA, Helmy, T , Azzedin, F & Al-Suhaim, A 2006, Evaluation of breast cancer tumor classification with unconstrained functional networks classifier. in IEEE International Conference on Computer Systems and Applications, 2006., 1618367, IEEE International Conference on Computer Systems and Applications, 2006, vol. 2006, IEEE Computer Society, pp. 281-287. https://doi.org/10.1109/aiccsa.2006.205102

Evaluation of breast cancer tumor classification with unconstrained functional networks classifier. / El-Sebakhy, Emad A.; Faisal, Kanaan Abed; Helmy, T. et al.
IEEE International Conference on Computer Systems and Applications, 2006. IEEE Computer Society, 2006. p. 281-287 1618367 (IEEE International Conference on Computer Systems and Applications, 2006; Vol. 2006).

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

TY - GEN

T1 - Evaluation of breast cancer tumor classification with unconstrained functional networks classifier

AU - El-Sebakhy, Emad A.

AU - Faisal, Kanaan Abed

AU - Helmy, T.

AU - Azzedin, F.

AU - Al-Suhaim, A.

PY - 2006

Y1 - 2006

N2 - This paper proposes functional networks as an unconstrained classifier scheme for multivariate data to diagnose the breast cancer tumor. The performance of this new technique is measured using two well known databases under the minimum description length criterion, the results are compared with the most common existing classifiers in both computer science and statistics literatures. This new classifier shown reliable and efficient results with better correct classification rate, and much less computational time.

AB - This paper proposes functional networks as an unconstrained classifier scheme for multivariate data to diagnose the breast cancer tumor. The performance of this new technique is measured using two well known databases under the minimum description length criterion, the results are compared with the most common existing classifiers in both computer science and statistics literatures. This new classifier shown reliable and efficient results with better correct classification rate, and much less computational time.

KW - Breast cancer detection

KW - Functional networks

KW - Minimum description length

KW - Pattern classification

UR - https://www.scopus.com/pages/publications/33750810834

U2 - 10.1109/aiccsa.2006.205102

DO - 10.1109/aiccsa.2006.205102

M3 - Conference contribution

AN - SCOPUS:33750810834

SN - 1424402123

SN - 9781424402120

T3 - IEEE International Conference on Computer Systems and Applications, 2006

SP - 281

EP - 287

BT - IEEE International Conference on Computer Systems and Applications, 2006

PB - IEEE Computer Society

ER -

El-Sebakhy EA, Faisal KA, Helmy T , Azzedin F, Al-Suhaim A. Evaluation of breast cancer tumor classification with unconstrained functional networks classifier. In IEEE International Conference on Computer Systems and Applications, 2006. IEEE Computer Society. 2006. p. 281-287. 1618367. (IEEE International Conference on Computer Systems and Applications, 2006). doi: 10.1109/aiccsa.2006.205102

Evaluation of breast cancer tumor classification with unconstrained functional networks classifier

Abstract

Publication series

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this