Title: Fuzzy soft set approach for classifying malignant and benign breast tumours

Authors: S. Sreedevi; Elizabeth Sherly

Addresses: University of Kerala, Thiruvanandapuram, Kerala, 695-581, India ' Indian Institute of Information Technology and Management, Kerala, Technopark, Thiruvanandapuram, Kerala, 695 581, India

Abstract: Breast cancer is one of the most common health problems faced by women all over the world and mammography is an effective technique used for its early detection. This work is concentrated on developing machine learning algorithms combined with a mathematical model for classifying malignant or benign images in digital mammograms. The mathematical concept of fuzzy soft set theory is advocated here, which is an extension of crisp and fuzzy with parameterisation. Even though fuzzy and other soft computing techniques have made great progress in solving complex systems that involve uncertainties, imprecision and vagueness, the theory of soft sets opens up a new way for managing uncertain data with parameterisation. The classification is performed by using fuzzy soft aggregation operator to identify abnormality in a mammogram image as malignant or benign. This work is a fully automated computer aided detection method which involves automated noise removal, pectoral muscles removal, segmentation of ROI, identification of micro calcification clusters, feature extraction and feature selection followed by classification. The experiment is performed on images from MIAS dataset and gives 95.12% accuracy.

Keywords: digital mammography; computer-aided diagnosis; CAD; fuzzy soft set theory; fuzzy c-means; NL-means; fuzzy soft aggregation operator.

DOI: 10.1504/IJHTM.2021.119158

International Journal of Healthcare Technology and Management, 2021 Vol.18 No.3/4, pp.228 - 249

Received: 01 Aug 2016
Accepted: 09 May 2017
Published online: 26 Nov 2021 *

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