# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 5 Experiment
### Discussion  
Overall, these results underscore the significance of selecting the suitable CAM method to improve the interpretability of classification models. The structure and characteristics of each model may be more suited to certain specific CAM methods, and this adaptability plays a key role in achieving optimal model interpretation. This discovery provides important guidance for future research, this suggests that the characteristics of the model must be taken into account when implementing CAM methods to achieve optimal interpretative outcomes. The specific architecture of the model also needs to be considered. Different models may have different adaptabilities and responses to different CAM methods, and this should be fully considered when applying CAM methods. Moreover, this also suggests that when dealing with models with complex architectures, such as GoogleNet, more advanced or specialized visualization techniques may need to be developed to obtain more accurate and comprehensive interpretations. The compatibility between specific CAM methods and model structures plays a crucial role in achieving optimal interpretability. Our study commenced by selecting five medical image datasets across various disease domains: skin cancer, breast cancer, chest X-rays, diabetic retinopathy (DR), and brain tumors, all publicly available. We evaluated the stability of four contemporary classification models across these datasets. Each model performed classification tasks on all five datasets. The results indicated that the type of dataset influences the classification outcomes. Notably, the HAM dataset consistently demonstrated robust performance across all models. However, the DR and breast cancer datasets showed lower performance metrics in areas other than accuracy, likely due to their imbalanced nature. In contrast, the models exhibited stable performance across the remaining datasets, efficiently managing the corresponding disease classifications. Overall, our findings suggest that despite real-world case imbalances, the chosen models can reliably perform classification tasks. Subsequently, we assessed various Class Activation Mapping (CAM) methods on datasets with segmentation tasks. This involved comparative experiments analyzing \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **GradCAM** & **GradCAM++** & **XGradCAM** & **AblationCAM** & **EigenCAM** & **EigenGradCAM** & **LayerCAM** & **FullGrad** \\ \hline DenseNet121-a & 0.29 & 0.269 & 0.178 & 0.297 & 0.36 & **0.372** & 0.24 & 0.085 \\ EfficientNet, b3-a & **0.307** & 0.266 & **0.307** & 0.295 & 0.005 & 0.007 & 0.239 & 0.273 \\ GoogleNet-a & 0 & 0 & 0 & 0.011 & 0.181 & **0.192** & 0 & \\ ResNet50-a & 0.27 & 0.263 & 0.27 & **0.271** & 0.26 & 0.269 & 0.254 & 0.192 \\ DenseNet121-b & 0.376 & 0.599 & 0.369 & 0.457 & 0.566 & 0.594 & 0.587 & **0.651** \\ EfficientNet, b3-b & 0.46 & 0.471 & 0.46 & 0.471 & 0.039 & 0.046 & 0.495 & **0.762** \\ GoogleNetb & 0.132 & 0.174 & 0.132 & 0.133 & 0.056 & 0.094 & 0.211 & \\ ResNet50-b & 0.023 & 0.244 & 0.023 & 0.036 & 0.017 & 0.019 & 0.376 & **0.455** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Average IoU results of eight explainable methods. CAM images against corresponding mask images within the datasets. We focused on the breast cancer and HAM datasets, testing eight different CAM methods. The results varied across different models and datasets, indicating that no single method consistently outperformed others. In particular, the EfficientNet-B3 model, combined with the FullGrad explanation method, achieved the highest Intersection over Union (IoU) score of 0.762 in the skin cancer dataset, underscoring FullGrad's significant interpretive advantage in this classification task. Notably, FullGrad performed well across all models except GoogleNet, which struggled with its complex multi-branch structure. GradCAM consistently performs well across various models, maintaining IoU metrics at or above the median level.