Innovative breast cancer detection using a segmentation-guided ensemble classification framework.

ABSTRACT: Breast cancer (BC) remains a significant global health issue, necessitating innovative methodologies to improve early detection and diagnosis. Despite the existence of intelligent deep learning models, their efficacy is often limited due to the oversight of small-sized masses, leading to false positive and false negative outcomes. This research introduces a novel segmentation-guided classification model developed to increase BC detection accuracy. The designed model unfolds in two critical phases, each contributing to a comprehensive BC diagnostic pipeline. In Phase I, the Attention U-Net model is utilized for BC segmentation. The encoder extracts hierarchical features, while the decoder, supported by attention mechanisms, refines the segmentation, focusing on suspicious regions. In Phase

SUBMITTER: Bala PM 

PROVIDER: S-EPMC11704121 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature