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

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 II, a novel ensemble approach is introduced for BC classification, involving various feature extraction methods, base classifiers, and a meta-classifier. An ensemble of model classifiers-including support vector machine, decision trees, k-nearest neighbor and artificial neural network- captures diverse patterns within these features. The Random Forest meta-classifier amalgamates their outputs, leveraging their collective strengths. The proposed integrated model accurately identifies different breast tumor classes, including malignant, benign, and normal. The precise region-of-interest analysis from segmentation phase significantly boosted classification performance of ensemble meta-classifier. The model accomplished an overall accuracy rate of 99.57% with high segmentation performance of 95% f1-score, illustrating its high discriminative power in detecting malignant, benign, and normal cases within the ultrasound image dataset. This research contributes to reducing breast tumor morbidity and mortality by facilitating early detection and timely intervention, ultimately supporting better patient outcomes.

The online version contains supplementary material available at 10.1007/s13534-024-00435-7.