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

Fangyi Li, Changjing Shang, Ying Li, Qiang Shen

Research output: Contribution to conferencePaperpeer-review

2 Citations (SciVal)


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
Publication statusPublished - 14 Oct 2018
EventFuzzy Systems - Rio de Janeiro, Brazil
Duration: 08 Jul 201813 Jul 2018
Conference number: 27


ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2018
CityRio de Janeiro
Period08 Jul 201813 Jul 2018


  • shape
  • feature extraction
  • interpolation
  • cognition
  • training
  • medical diagnostic imaging


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