Abstract

Breast cancer is a prevalent and potentially life-threatening disease affecting women globally. Early and accurate detection of breast lesions through medical imaging, such as ultrasound, is crucial for effective treatment. In this study, we propose a novel approach for the classification of breast ultrasound images using a fuzzy-rank ensemble network. The proposed ensemble network combines the strengths of fuzzy logic and rank-based techniques to enhance the robustness and accuracy of classification. The network leverages fuzzy membership functions to capture the uncertainty inherent in ultrasound image interpretation, while the rank-based ensemble method aggregates predictions from multiple classifiers to improve overall performance. Experimental results on a comprehensive dataset demonstrate that the proposed fuzzy-rank ensemble network achieves superior classification performance compared to individual classifiers and traditional ensemble methods. This approach holds promise for improving the diagnostic capabilities of breast ultrasound image analysis, ultimately aiding clinicians in making more informed decisions and potentially contributing to enhanced patient outcomes.