  The Deep learning (DL) models for diagnosing breast cancer from mammographic
images often operate as "black boxes", making it difficult for healthcare
professionals to trust and understand their decision-making processes. The
study presents 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. The 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, Inception-V3 and ResNet was employed. A focal point of
our study is the evaluation of XAI's effectiveness in interpreting model
predictions, highlighted by utilizing the Hausdorff measure to assess the
alignment between AI-generated explanations and expert annotations
quantitatively. This approach is critical 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 extended
well beyond the current methodologies. It encourages further research into how
to combine multimodal data and improve AI explanations to meet the needs of
clinical practice.
