Adaptive Fuzzy Transformation for Abnormal Breast Mass Detection

Abstract

Breast mass detection remains a significant challenge in developing effective computer-aided diagnosis (CADx) systems to assist clinicians in differentiating between benign and malignant masses. This paper introduces a novel fuzzy rule-based CADx approach for mammographic mass classification, utilising Transformation-based Fuzzy Rule Interpolation with Mahalanobis matrices (MT-FRI). This method enables reliable and interpretable classification by transforming attributes into a new feature space and interpolating for unmatched cases, making it well-suited to limited-data scenarios. The proposed approach integrates a structured pipeline encompassing feature extraction, feature selection, fuzzy rule generation, and interpolation inference, all designed to enhance transparency in diagnostic decisions. The system implementing the approach is evaluated on four widely-used mammographic datasets—INbreast, CBIS-DDSM, BCDR-D01, and BCDR-F01. For the first time, comparative experiments demonstrate that state-of-the-art fuzzy rule interpolative methods, particularly MT-FRI, achieve superior classification performance over representative classical machine learning models and deep neural networks. Unlike deep learning models, which require extensive labelled data and function as ”black boxes”, MT-FRI produces transparent, human-readable rules, supporting clinical interpretability. This work underscores the potential of MT-FRI as an adaptable and interpretable CADx solution for mammographic diagnosis, especially valuable in sparse-data environments.