Enhancing mammogram classification using explainable Conditional Self-Attention Generative Adversarial Network

Abstract

Globally, breast cancer is one of the leading causes of death in women. Thus, there is an urgent requirement for precise and comprehensible diagnostic instruments. This paper introduces a new deep learning model, an Explainable Conditional Self-Attention Generative Adversarial Network (ExCSA-GAN), suggested for the classification of breast cancer using mammography images. The used input mammograms were drawn from the publicly available CBIS-DDSM breast cancer image dataset and the LHD dataset. Noise in the images is minimized with Window-Aware Guided Bilateral Filtering (WAGBF). These images are then further segmented for cancerous regions through the use of the Median-Average 2D Otsu’s Otsu-based segmentation (MA-2D-O). Finally, classification is done using ExCSA-GAN, which performs well on the target classification metric while being interpretable. The model hyperparameters are fine-tuned using the Greylag Goose Optimization (GGO) algorithm, which leads to optimal performance. To enhance the transparency of predictions, the proposed approach integrates four explainable algorithms: Gradient-Weighted Class Activation Mapping (Grad-CAM), Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Layer-Wise Relevance Propagation (LRP). Comparing ExCSA-GAN to traditional deep learning models, experimental findings show that it improves accuracy by 9.8% and reduces false negative rate (FNR) by 12.5%. The superiority of the proposed approach is validated by experimental results using the core metrics, which include accuracy, Matthews Correlation Coefficient (MCC), precision, sensitivity, specificity, F-measure, computational complexity, and computation time. This approach offers better performance and improved interpretability for clinical applications.