Feature ranking-guided fuzzy rule interpolation for mammographic mass shape classification

Fangyi Li, Changjing Shang, Ying Li, Qiang Shen

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

2 Scopus citations

Abstract

This paper presents a novel fuzzy rule-based interpolative reasoning system for mammographic mass shape classification that is interpretable to medical professionals. In particular, a feature ranking-guided fuzzy rule interpolation (FRI) method is embedded in the proposed system to make inference possible given a sparse rule base, which may occur in dealing with insufficient mammographic image data (and indeed in coping with many other computer-aided medical diagnostic problems). The rule base for inference is learned from a set of labelled morphological features which are extracted from mass shapes. A classical FRI mechanism is integrated with a procedure for feature selection to score the individual rule antecedents in the inducted sparse rule base for more accurate interpolative reasoning. The work is evaluated on a real-world mammographic image data base with promising results, demonstrating the efficacy of the proposed fuzzy rule-based interpolative classification system.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060207
DOIs
StatePublished - 12 Oct 2018
Event2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

NameIEEE International Conference on Fuzzy Systems
Volume2018-July
ISSN (Print)1098-7584

Conference

Conference2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018
Country/TerritoryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

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