Paper Info
Title
Feature Ranking-Guided Fuzzy Rule Interpolation for Mammographic Mass Shape Classification
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.
Year
DOI
Venue
2018
10.1109/FUZZ-IEEE.2018.8491598
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Keywords
Field
DocType
feature ranking-guided fuzzy rule interpolation,mammographic mass shape classification,computer-aided medical diagnostic problems,fuzzy rule-based interpolative classification system,morphological features extraction,rule antecedents,sparse rule base,fuzzy rule-based interpolative reasoning system,mammographic image data base,FRI method,inference,feature selection
Fuzzy rule interpolation,Feature selection,Inference,Computer science,Feature ranking,Interpolation,Feature extraction,Artificial intelligence,Reasoning system,Machine learning,Fuzzy rule
Conference
ISBN
Citations 
PageRank 
978-1-5090-6021-4
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Fangyi Li1246.13
Changjing Shang221234.92
Ying Li313021.36
Qiang Shen4187894.48