## 2 Related Work  
Class activation mapping (CAM) visualizes region-specific contributions to model predictions in CNNs. As a foundational XAI tool, CAM enhances model transparency by revealing decision-critical features in vision tasks like image segmentation. **Definition 1**. : _(Class Activation Map) CAM was initially introduced to identify important pixels in images determined by CNN models. It creates a heat map, highlighting the regions that affect the network's predictions by projecting the weights of the model's output layers onto a feature map of a convolutional layer (usually the last layer). For interest Class \(c\), the CAM explanation is defined as follows:_  
\[CAM=ReLU\left(\sum_{k}a_{k}^{c}A_{I-1}^{k}\right), \tag{1}\]  
_where_  
\[\alpha_{k}^{c}=\omega_{I,I+1}^{c}[k], \tag{2}\]  
\(\omega_{I,I+1}^{c}[k]\) _is the weight of the \(k\)th neuron after pooling._  
Figure 1: Visualization of our proposed method along with Fullgrad CAM [20], Grad-CAM [23], Grad-CAM++ [22], HiResCAM [23], LayerCAM [27], and Score-CAM [28]. The original CAM framework [20] pioneered CNN decision visualization by mapping convolutional features to classification results via global average pooling (GAP). Its mathematical explicability required minimal architectural changes (replacing fully connected layers with GAP + linear layers [32]) but suffered from inflexible GAP-dependence [20] and single-scale feature limitations causing coarse localization [22]. Definition 2: (Grad-CAM) _Grad-CAM is a prominent variant of CAM that uses the gradients of the target concept flowing into the last convolutional layer to weight the importance of each spatial location. The core formula for Grad-CAM is given by_  
\[Grad-CAM=ReLU\Big{(}\sum_{k}\alpha_{k}^{c}A_{l-1}^{k}\Big{)}, \tag{3}\]  
_where_  
\[\alpha_{k}^{c}=GAP(\frac{\partial Y^{c}}{\partial A_{l}^{k}}), \tag{4}\]  
\(GAP(\cdot)\) _denotes the global pooling operation. \(A_{l-1}^{k}\) represents the activation mapping of the \(k\)th channel in the \(l\)th layer and \(Y^{c}\) is the probability distribution output of category \(c\) by the network._  
Grad-CAM overcomes these limitations through a gradient-based feature weighting approach [33], enabling architecture-agnostic applications while preserving spatial coherence for higher-resolution activation maps [32]. However, it introduced gradient saturation in ReLU networks and insensitivity to discriminative negative gradients [34]. Score-CAM further advances the field of XAI by providing more accurate and reliable visualizations of model predictions. By addressing the gradient saturation problem and incorporating forward-pass importance scoring, it offers a more comprehensive view of the features that contribute to a model's decisions. This enhancement is crucial for building trust in AI systems and enabling more effective model validation and refinement in real-world applications. Definition 3: (Score-CAM) Score-CAM extends Grad-CAM by using the score function's gradient rather than the logits' gradient, aiming to provide more precise localization [35]. The core formula for Score-CAM (the structure diagram is shown in Figure 2) is  
\[Score-CAM=ReLU\Big{(}\sum_{k}\alpha_{k}^{c}A_{l}^{k}\Big{)}, \tag{5}\]  
where  
\[\alpha_{k}^{c}=C\Big{(}A_{l}^{k}\Big{)}, \tag{6}\]  
\(C(\cdot)\) denotes the channel-wise increase of confidence (CIC) for activation map \(A_{l}^{k}\). CIC is defined as  
\[C\Big{(}A_{l}^{k}\Big{)}=f\Big{(}X\circ H_{l}^{k}\Big{)}-f(X_{b}), \tag{7}\]  
where  
\[H_{l}^{k}=s\Big{(}Up\Big{(}A_{l}^{k}\Big{)}\Big{)}, \tag{8}\]  
\(s(\cdot)\) is the normalization function that maps each element in the input matrix to [0, 1], and \(Up\Big{(}A_{l}^{k}\Big{)}\) denotes the operation that upsamples \(A_{l}^{k}\) into the input size. \(X_{b}\) is the baseline, and \(X\) is the input. Score-CAM replaced gradients with perturbation-based importance scoring, eliminating gradient artifacts and improving noise robustness via multi-pass averaging [23].