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Grad-CAM (Gradient-weighted Class Activation Mapping): A systematic literature review
Abdul Muiz Fayyaz1, Said Jadid Abdulkadir2, Noureen Talpur2
1Department of Computing, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia.
Computers in Biology and Medicine
|October 18, 2025
Summary
This study reviews Gradient-weighted Class Activation Mapping (Grad-CAM), a key Explainable Artificial Intelligence (XAI) method. It highlights Grad-CAM's evolution and applications, particularly in medical imaging for enhanced model interpretability.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Computer Vision
Background:
- Explainable Artificial Intelligence (XAI) is vital for trust in Machine Learning (ML) and Deep Learning (DL) models.
- Gradient-weighted Class Activation Mapping (Grad-CAM) is a prominent XAI technique for interpreting Convolutional Neural Networks (CNNs).
- Grad-CAM visually identifies image regions crucial for CNN decision-making.
Purpose of the Study:
- To conduct a Systematic Literature Review (SLR) on Gradient-weighted Class Activation Mapping (Grad-CAM).
- To analyze Grad-CAM advancements, particularly in medical imaging.
- To explore Grad-CAM applications within ML and DL frameworks.
Main Methods:
- Systematic literature search across major academic databases (Scopus, Web of Science, IEEE Xplore, ScienceDirect).
- In-depth examination of 51 selected peer-reviewed publications from a pool of 427 identified articles.
- Analysis focused on the period 2020-2024.
Main Results:
- Comprehensive overview of Grad-CAM's evolution and current research trends.
- Identification of various Grad-CAM techniques and their integration with diverse ML/DL architectures.
- Detailed insights into Grad-CAM optimization strategies and its impact on model interpretability.
Conclusions:
- Grad-CAM is a significant tool for enhancing transparency in CNNs, especially in medical imaging.
- The review provides a valuable resource for understanding Grad-CAM's capabilities and future directions.
- Further research can leverage Grad-CAM for improved diagnostic accuracy and trustworthy AI in healthcare.
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