Grad++ScoreCAM: Enhancing Visual Explanations of Deep Convolutional Networks Using Incremented Gradient and Score- Weighted Methods

We propose a novel method that combines the strengths of two popular class activation mapping techniques, GradCAM++ and ScoreCAM, to improve the interpretability and localization of convolutional neural networks (CNNs). Our proposed method, called "Grad++-ScoreCAM", first utilizes the GradCAM++ algorithm to generate a coarse heatmap of an input image, highlighting the regions of importance for a particular class. Then, we employ the ScoreCAM algorithm to refine the heatmap by incorporating the localization information from the intermediate layers of the network. By combining these two techniques, we can generate more accurate and fine-grained heatmaps that highlight the regions of the input image that are most relevant to the prediction of the CNN. We evaluate our proposed method on a benchmark dataset and demonstrate its superiority over existing methods in terms of accuracy and interpretability. Our method has potential applications in various fields, including medical imaging, object recognition, and natural language processing.