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A Genetic Programming Approach to Automatically Construct Informative Attributes for Mammographic Density Classification

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conference contribution
posted on 2023-03-13, 09:32 authored by QUI Ain, Bing XueBing Xue, Harith Al-SahafHarith Al-Sahaf, Mengjie ZhangMengjie Zhang
Breast density is widely used as an initial indicator of developing breast cancer. At present, current classification methods for mammographic density usually require manual operations or expert knowledge that makes them expensive in real-time situations. Such methods achieve only moderate classification accuracy due to the limited model capacity and computational resources. In addition, most existing studies focus on improving classification accuracy using only raw images or the entire set of original attributes and remain unable to identify hidden patterns or causal information necessary to discriminate breast density classes. It is challenging to find high-quality knowledge when some attributes defining the data space are redundant or irrelevant. In this study, we present a novel attribute construction method using genetic programming (GP) for the task of breast density classification. To extract informative features from the raw mammographic images, wavelet decomposition, local binary patterns, and histogram of oriented gradients are utilized to include texture, local and global image properties. The study evaluates the goodness of the proposed method on two benchmark real-world mammographic image datasets and compares the results of the proposed GP method with eight conventional classification methods. The experimental results reveal that the proposed method significantly outperforms most of the commonly used classification methods in binary and multi-class classification tasks. Furthermore, the study shows the potential of G P for mammographic breast density classification by interpreting evolved attributes that highlight important breast density characteristics.

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Preferred citation

Ain, Q. U. I., Xue, B., Al-Sahaf, H. & Zhang, M. (2022, January). A Genetic Programming Approach to Automatically Construct Informative Attributes for Mammographic Density Classification. In IEEE International Conference on Data Mining Workshops, ICDMW 2022 IEEE International Conference on Data Mining Workshops (ICDMW) (2022-November pp. 378-387). IEEE. https://doi.org/10.1109/ICDMW58026.2022.00057

Conference name

2022 IEEE International Conference on Data Mining Workshops (ICDMW)

Conference start date

2022-11-28

Conference finish date

2022-12-01

Title of proceedings

IEEE International Conference on Data Mining Workshops, ICDMW

Volume

2022-November

Publication or Presentation Year

2022-01-01

Pagination

378-387

Publisher

IEEE

Publication status

Published

ISSN

2375-9232

eISSN

2375-9259

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