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

Ping Zhang*, Kuldeep Kumar, Brijesh Verma

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2 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 languageEnglish
Title of host publicationFuzzy Systems and Knowledge Discovery
Subtitle of host publication FSKD 2005
EditorsL Wang, Y Jin
PublisherSpringer
Pages316-319
Number of pages4
Volume3614 LNAI
ISBN (Print)9783540283317
DOIs
Publication statusPublished - 2005
EventInternational Conference on Fuzzy Systems and Knowledge Discovery - Changsa, China
Duration: 27 Aug 200529 Aug 2005
Conference number: 2nd

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3614 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

ConferenceInternational Conference on Fuzzy Systems and Knowledge Discovery
Abbreviated titleFSKD 2005
CountryChina
CityChangsa
Period27/08/0529/08/05

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  • 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: FSKD 2005 (Vol. 3614 LNAI, 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