TY - GEN
T1 - Feature ranking-guided fuzzy rule interpolation for mammographic mass shape classification
AU - Li, Fangyi
AU - Shang, Changjing
AU - Li, Ying
AU - Shen, Qiang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/12
Y1 - 2018/10/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85060477387&partnerID=8YFLogxK
U2 - 10.1109/FUZZ-IEEE.2018.8491598
DO - 10.1109/FUZZ-IEEE.2018.8491598
M3 - 会议稿件
AN - SCOPUS:85060477387
T3 - IEEE International Conference on Fuzzy Systems
BT - 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018
Y2 - 8 July 2018 through 13 July 2018
ER -