A hybrid classifier for mass classification with different kinds of features in mammography

Ping Zhang; Kuldeep Kumar; Brijesh Verma

doi:10.1007/11540007_38

A hybrid classifier for mass classification with different kinds of features in mammography

Ping Zhang^*, Kuldeep Kumar, Brijesh Verma

^*Corresponding author for this work

Bond Business School

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

11 Citations (Scopus)

Abstract

This paper proposes a hybrid system which combines computer extracted features and human interpreted features from the mammogram, with the statistical classifier's output as another kind of features in conjunction with a genetic neural network classifier. The hybrid system produced better results than the single statistical classifier and neural network. The highest classification rate reached 91.3%. The area value under the ROC curve is 0.962. The results indicated that the mixed features contribute greatly for the classification of mass patterns into benign and malignant.

Original language	English
Title of host publication	Fuzzy Systems and Knowledge Discovery
Subtitle of host publication	Second International Conference, FSKD 2005, Changsha, China, August 27-29, 2005, Proceedings, Part II
Editors	L Wang, Y Jin
Publisher	Springer
Pages	316-319
Number of pages	4
ISBN (Electronic)	978-3-540-31828-6
ISBN (Print)	9783540283317
DOIs	https://doi.org/10.1007/11540007_38
Publication status	Published - 2005
Event	International Conference on Fuzzy Systems and Knowledge Discovery - Changsa, China Duration: 27 Aug 2005 → 29 Aug 2005 Conference number: 2nd

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3614 LNAI
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	International Conference on Fuzzy Systems and Knowledge Discovery
Abbreviated title	FSKD 2005
Country/Territory	China
City	Changsa
Period	27/08/05 → 29/08/05

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10.1007/11540007_38

Cite this

Zhang, P., Kumar, K., & Verma, B. (2005). A hybrid classifier for mass classification with different kinds of features in mammography. In L. Wang, & Y. Jin (Eds.), Fuzzy Systems and Knowledge Discovery: Second International Conference, FSKD 2005, Changsha, China, August 27-29, 2005, Proceedings, Part II (pp. 316-319). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3614 LNAI). Springer. https://doi.org/10.1007/11540007_38

Zhang, Ping ; Kumar, Kuldeep ; Verma, Brijesh. / A hybrid classifier for mass classification with different kinds of features in mammography. Fuzzy Systems and Knowledge Discovery: Second International Conference, FSKD 2005, Changsha, China, August 27-29, 2005, Proceedings, Part II. editor / L Wang ; Y Jin. Springer, 2005. pp. 316-319 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{72e35fdded9f458aa03ffd2d9fcfb838,

title = "A hybrid classifier for mass classification with different kinds of features in mammography",

abstract = "This paper proposes a hybrid system which combines computer extracted features and human interpreted features from the mammogram, with the statistical classifier's output as another kind of features in conjunction with a genetic neural network classifier. The hybrid system produced better results than the single statistical classifier and neural network. The highest classification rate reached 91.3%. The area value under the ROC curve is 0.962. The results indicated that the mixed features contribute greatly for the classification of mass patterns into benign and malignant.",

author = "Ping Zhang and Kuldeep Kumar and Brijesh Verma",

year = "2005",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "316--319",

editor = "L Wang and Y Jin",

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note = "International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005 ; Conference date: 27-08-2005 Through 29-08-2005",

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Zhang, P, Kumar, K & Verma, B 2005, A hybrid classifier for mass classification with different kinds of features in mammography. in L Wang & Y Jin (eds), Fuzzy Systems and Knowledge Discovery: Second International Conference, FSKD 2005, Changsha, China, August 27-29, 2005, Proceedings, Part II. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3614 LNAI, Springer, pp. 316-319, International Conference on Fuzzy Systems and Knowledge Discovery, Changsa, China, 27/08/05. https://doi.org/10.1007/11540007_38

A hybrid classifier for mass classification with different kinds of features in mammography. / Zhang, Ping; Kumar, Kuldeep; Verma, Brijesh.
Fuzzy Systems and Knowledge Discovery: Second International Conference, FSKD 2005, Changsha, China, August 27-29, 2005, Proceedings, Part II. ed. / L Wang; Y Jin. Springer, 2005. p. 316-319 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3614 LNAI).

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

TY - GEN

T1 - A hybrid classifier for mass classification with different kinds of features in mammography

AU - Zhang, Ping

AU - Kumar, Kuldeep

AU - Verma, Brijesh

N1 - Conference code: 2nd

PY - 2005

Y1 - 2005

N2 - This paper proposes a hybrid system which combines computer extracted features and human interpreted features from the mammogram, with the statistical classifier's output as another kind of features in conjunction with a genetic neural network classifier. The hybrid system produced better results than the single statistical classifier and neural network. The highest classification rate reached 91.3%. The area value under the ROC curve is 0.962. The results indicated that the mixed features contribute greatly for the classification of mass patterns into benign and malignant.

AB - This paper proposes a hybrid system which combines computer extracted features and human interpreted features from the mammogram, with the statistical classifier's output as another kind of features in conjunction with a genetic neural network classifier. The hybrid system produced better results than the single statistical classifier and neural network. The highest classification rate reached 91.3%. The area value under the ROC curve is 0.962. The results indicated that the mixed features contribute greatly for the classification of mass patterns into benign and malignant.

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U2 - 10.1007/11540007_38

DO - 10.1007/11540007_38

M3 - Conference contribution

AN - SCOPUS:26944500190

SN - 9783540283317

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 319

BT - Fuzzy Systems and Knowledge Discovery

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T2 - International Conference on Fuzzy Systems and Knowledge Discovery

Y2 - 27 August 2005 through 29 August 2005

ER -

Zhang P, Kumar K, Verma B. A hybrid classifier for mass classification with different kinds of features in mammography. In Wang L, Jin Y, editors, Fuzzy Systems and Knowledge Discovery: Second International Conference, FSKD 2005, Changsha, China, August 27-29, 2005, Proceedings, Part II. Springer. 2005. p. 316-319. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11540007_38

A hybrid classifier for mass classification with different kinds of features in mammography

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