# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 5 Experiment
### Comparison of CAM Methods  
In our selection of CAM methods, we chose eight different CAM methods: Grad-CAM, GradCAM++, XGradCAM [72], AblationCAM, EigenCAM [73], EigenGrad-CAM, LayerCAM [74], and FullGrad [75]. Among these, Grad-CAM is currently the most frequently used CAM method in the medical industry, with the other seven being derivatives of the Grad-CAM method. The descriptions of these methods are shown in Table 10.  
In our study, we employed a novel experimental design to assess the efficacy of various CAM (Class Activation Mapping) methods on segmentation task datasets. We compared CAM-generated images with corresponding mask images by calculating their Intersection over Union (IoU). IoU is a standard metric that quantifies the overlap between two areas, critical for evaluating the interpretative effectiveness of CAM methods. A higher IoU value typically signifies greater interpretative reliability. The experimental results are displayed in Figure 12, and the corresponding IoU values are presented in Table 11. Figure 12 reveals the heatmaps obtained using four different models (DenseNet121, EfficientNet-B3, ResNet50, and GoogleNet) with the eight CAM methods for breast cancer classification tasks. These results clearly indicate that different CAM methods have varying interpretive effects on different models. Specifically, using the DenseNet121 model, the EigenCAM method showed the highest IoU score (0.372), indicating its superior interpretative capability on this model. In the EfficientNet-B3 model, GradCAM and XGradCAM methods both achieved the highest score (0.307), demonstrating their effectiveness on this model. For the ResNet50 model, AblationCAM performed the best, with an IoU score of 0.271. Finally, for the GoogleNet model, the EigenGradCAM method showed the best effect, scoring 0.192. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Classification** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline MEL & ResNet50 & 0.98 & 0.77 & 0.76 & 0.76 \\ NV & ResNet50 & 0.99 & 0.88 & 0.88 & 0.88 \\ BCC & ResNet50 & 0.97 & 0.9 & 0.85 & 0.87 \\ AKIEC & ResNet50 & 1 & 1 & 0.74 & 0.85 \\ BKL & ResNet50 & 0.96 & 0.87 & 0.79 & 0.83 \\ DF & ResNet50 & 0.96 & 0.95 & 0.98 & 0.97 \\ VASC & ResNet50 & 1 & 0.9 & 0.9 & 0.9 \\ AVERAGE & ResNet50 & **0.98** & **0.8957** & **0.8429** & **0.8657** \\ \hline \end{tabular}
\end{table} 
Table 9: _Cont._Figure 12: Comparison of the effects of different CAM methods (In the saliency map, colors transition from cooler to warmer tones, such as blue to red. Areas depicted in cooler colors are less influential or important to the model’s decision-making process, whereas warmer colors highlight regions of greater relevance). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **Description** & **Requires Network Modification** \\ \hline Grad-CAM & Uses convolutional layer gradients to create heatmaps, highlighting crucial image regions for decision-making. & No \\ \hline GradCAM++ & Improved Grad-CAM with refined localization and gradient weighting for multi-class tasks. & No \\ \hline XGradCAM & Adjusts Grad-CAM gradients and activation maps for better consistency and interpretability. & No \\ \hline Ablation-CAM & Generates activation maps by ablating convolutions; focuses on feature importance for decision-making. & Yes \\ \hline EigenCAM & Uses PCA for gradient-independent visualization of key features in convolutional layers. & No \\ \hline EigenGradCAM & Hybrid of EigenCAM’s PCA and Grad-CAM’s gradients for comprehensive activation maps. & Yes \\ \hline LayerCAM & Accumulates information across layers for a broader perspective on model decisions. & No \\ \hline FullGrad & Combines input image gradients and convolutions for detailed class activation maps. & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of Different CAM Methods.