B-CADNet: A Novel Deep Learning-Based Computer-Aided Diagnostic Framework for Enhanced Breast Cancer Detection in Histopathological Imaging

Abstract

Breast cancer remains one of the major causes of mortality among women, making early and precise diagnosis vital for improving survival outcomes. Histopathological analysis, though considered the gold standard, is labor-intensive and prone to observer variability. This paper presents B-CADNet, a novel deep learning-based computer-aided diagnostic framework designed for accurate breast cancer detection in histopathological images. The model incorporates an enhanced convolutional neural network with multi-scale feature extraction, contextual learning, and attention modules to effectively distinguish between benign and malignant tissues. Preprocessing techniques such as stain normalization and augmentation improve robustness and generalization. Experimental results on benchmark datasets demonstrate superior accuracy, sensitivity, specificity, and interpretability, highlighting B-CADNet’s potential as an efficient assistive tool in clinical workflows.