Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI
Maryam Ahmed, Tooba Bibi, Rizwan Ahmed Khan, Sidra Nasir · 2024
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 utilising 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.