Grad-CAM (Gradient-weighted Class Activation Mapping): A systematic literature review

Explainable Artificial Intelligence (XAI) has become a crucial aspect of modern Machine Learning (ML) and Deep Learning (DL) applications, emphasizing transparency and trust in model predictions. Among various XAI techniques, Gradient-weighted Class Activation Mapping (Grad-CAM) stands out for its ability to visually interpret Convolutional Neural Networks (CNNs) by highlighting image regions that contribute significantly to decision-making. This Systematic Literature Review (SLR) provides a comprehensive analysis of Grad-CAM, its advancements in medical imaging, and applications in ML and DL. The review explores current research trends, variations of Grad-CAM, and its integration with different ML/DL architectures. A systematic search across Scopus, Web of Science, IEEE Xplore, and ScienceDirect identified 427 peer-reviewed publications (2020-2024), of which 51 were selected for in-depth examination. This study offers valuable insights into the evolution of Grad-CAM, its optimization techniques, and its role in improving model interpretability in medical imaging analysis and related fields.