A ResNet-50 Classifier with Grad-CAM and SHAP for Explainable Breast Cancer Detection in Dense Mammography: Comparison with Human Radiologist Performance
This study demonstrates that a ResNet-50 classifier enhanced with Grad-CAM and SHAP explanations can surpass human radiologist sensitivity in detecting breast cancer within dense mammograms, achieving 72.2% sensitivity while providing actionable visual insights to support its deployment as a second-reader tool that flags high-confidence cases and defers ambiguous ones to clinicians.