Fusion-CAM is a novel framework that unifies gradient-based and region-based Class Activation Map (CAM) methods through a dedicated fusion mechanism. It aims to provide robust and highly discriminative visual explanations by first denoising gradient-based maps and then adaptively combining them with region-based maps to enhance class coverage and precision, outperforming existing CAM variants.