The study introduces an integrated framework combining Convolutional Neural Networks (CNNs) and Explainable Artificial Intelligence (XAI) for the enhanced diagnosis of breast cancer using the CBIS-DDSM dataset. Utilizing a fine-tuned ResNet50 architecture, our investigation not only provides effective differentiation of mammographic images into benign and malignant categories but also addresses the opaque "black-box" nature of deep learning models by employing XAI methodologies, namely Grad-CAM, LIME, and SHAP, to interpret CNN decision-making processes for healthcare professionals. Our methodology encompasses an elaborate data preprocessing pipeline and advanced data augmentation techniques to counteract dataset limitations, and transfer learning using pre-trained networks, such as VGG-16, DenseNet and ResNet was employed. A focal point of our study is the evaluation of XAI's effectiveness in interpreting model predictions, highlighted by utilising the Hausdorff measure to assess the alignment between AI-generated explanations and expert annotations quantitatively. This approach plays a critical role for XAI in promoting trustworthiness and ethical fairness in AI-assisted diagnostics. The findings from our research illustrate the effective collaboration between CNNs and XAI in advancing diagnostic methods for breast cancer, thereby facilitating a more seamless integration of advanced AI technologies within clinical settings. By enhancing the interpretability of AI-driven decisions, this work lays the groundwork for improved collaboration between AI systems and medical practitioners, ultimately enriching patient care. Furthermore, the implications of our research extend well beyond the current methodologies, advocating for subsequent inquiries into the integration of multimodal data and the refinement of AI explanations to satisfy the needs of clinical practice.

翻译：本研究提出了一种融合卷积神经网络（CNN）与可解释人工智能（XAI）的综合框架，基于CBIS-DDSM数据集实现乳腺钼靶影像增强诊断。通过采用微调后的ResNet50架构，本研究不仅成功实现对乳腺钼靶图像良恶性类别的有效区分，更针对深度学习模型固有的“黑箱”特性，应用Grad-CAM、LIME及SHAP等XAI方法，为医疗专业人员解读CNN决策过程提供可解释性支撑。本方法论包含精细的数据预处理流程与先进的数据增强技术以应对数据集局限性，并采用基于VGG-16、DenseNet及ResNet等预训练网络的迁移学习策略。研究重点聚焦于评估XAI在解释模型预测中的有效性，创新性地引入Hausdorff度量定量分析AI生成解释与专家标注的一致性。该方法对确立XAI在促进AI辅助诊断可信度与伦理公平性方面具有关键作用。研究结果表明，CNN与XAI在推进乳腺癌诊断方法中展现出有效协同效应，为高级AI技术在临床场景的更无缝整合提供支撑。通过增强AI驱动决策的可解释性，本研究为AI系统与医疗从业者的深度协作奠定基础，最终提升患者诊疗质量。此外，本研究的意义超越当前方法论范畴，倡导后续探索多模态数据融合及优化AI解释机制以满足临床实践需求。