An Explainable Attention Enhanced Model for Breast Cancer Detection

Breast cancer is a major worldwide health concern, where delayed or erroneous diagnosis critically worsens patient outcomes and increases mortality. Therefore, early and accurate detection of breast cancer is necessery to improve patient outcomes and lower mortality rates. Conventional diagnostic workflows, which depend heavily on manual histopathological and ultrasound image analysis are time consuming, subjective and prone to human error, specially as patient volumes continue to rise. Existing computer aided diagnosis (CAD) systems also have notable limitations like traditional machine learning (ML) classifiers depend on manually crafted features that do not generalize well across different magnifications and staining variations, while standard convolutional architectures treat every spatial position and channel equally, offering minimal insight into their own decision making processes and limiting clinical trust and adoption. To address these challenges, we propose an explainable framework combining DenseNet201 with a Convolutional Block Attention Module (CBAM) and a Transformer encoder for the classification of breast cancer histopathology images into benign and malignant categories. The integrated attention mechanism enables the model to selectively focus on diagnostically relevant spatial and channel-wise features, while Gradient weighted Class Activation Mapping (Grad-CAM) is employed to visualize these regions, enhancing interpretability and clinical trust. Comprehensive experiments demonstrate that our model achieves a high accuracy of 97.22%, significantly outperforming eight independently benchmarked backbone networks as well as reported traditional machine learning baselines, highlighting its potential as a reliable and interpretable tool for clinical decision support.