Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI

arXiv:2404.03892v1 Announce Type: cross
Abstract: 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. …

abstract architecture artificial artificial intelligence arxiv cancer cancer diagnosis cnns convolutional neural networks cs.ai cs.cv cs.lg dataset diagnosis differentiation eess.iv evaluation explainable ai explainable artificial intelligence framework integration intelligence investigation mammography networks neural networks resnet50 study type xai