  Explainable Deep Learning has gained significant attention in the field of
artificial intelligence (AI), particularly in domains such as medical imaging,
where accurate and interpretable machine learning models are crucial for
effective diagnosis and treatment planning. Grad-CAM is a baseline that
highlights the most critical regions of an image used in a deep learning
model's decision-making process, increasing interpretability and trust in the
results. It is applied in many computer vision (CV) tasks such as
classification and explanation. This study explores the principles of
Explainable Deep Learning and its relevance to medical imaging, discusses
various explainability techniques and their limitations, and examines medical
imaging applications of Grad-CAM. The findings highlight the potential of
Explainable Deep Learning and Grad-CAM in improving the accuracy and
interpretability of deep learning models in medical imaging. The code is
available in (will be available).
