# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 4 Results and Discussion of Selected Articles
### Performance Analysis  
In all selected studies, CNN, Recurrent Neural Networks (RNN), and Deep Neural Networks (DNN) served as the primary components of their models for disease detection and classification. Specific models used in these studies included Residual Networks (Resnet) [61], Dense Networks (Dense Net), Dilated Convolutional Neural Networks (DCNN), Bag of Features (BO), Visual Geometry Group (VGG), Efficient Networks (Efficient Net) [62], 3D Residual Networks (3D Resnet), U-Net, Res2 Net, and Inception-Resnet-v2.  
Through a comprehensive review of 45 relevant articles, we collected and analyzed the performance of various models mentioned in these articles, as shown in Figure 7. Figure 7 presents the highest accuracy achieved in the models used in each article. It is worth noting that 82% of the studies demonstrated high-level model accuracy exceeding 90%, indicating that the application of activation mapping methods in various domains can significantly enhance model performance. Only 5% of the articles reported a model accuracy below 80%. However, one study on EEG classification for Parkinson's disease showed lower performance with an accuracy of only 61%, mainly due to the relatively small size of the dataset used, containing only 90 samples.  
Additionally, some studies, apart from using accuracy as a performance reference metric, also employed other performance reference metrics, as shown in Table 4.  
In Table 4, Intersection over Union (IoU) [63] is a metric used to measure the performance of computer vision tasks such as object detection and semantic segmentation. Its purpose is to assess the degree of overlap between the predicted bounding boxes (or segmentation masks) and the actual target bounding boxes.  
Figure 7: The accuracy of each study [16; 17; 18; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 45; 47; 48; 50; 51; 52; 53; 54; 55; 56; 58; 59].  
AUC (Area Under the ROC Curve) [64] is used to measure the performance of binary classification models at different thresholds. It provides a comprehensive evaluation of a model's ability to classify positive and negative instances and is commonly used for comparing the performance of different models.  
The F1 score [65] is the harmonic mean of precision and recall and is used to provide a comprehensive assessment of a model's accuracy and coverage. It is a performance metric particularly suitable for problems with imbalanced class distributions.  
Specificity refers to the proportion of correctly identified negative cases out of all actual negative cases in binary classification. It is used to evaluate the model's performance in correctly identifying actual negative cases, i.e., the model's ability to correctly identify negative instances that actually do not exist.  
Sensitivity, in binary classification, represents the proportion of correctly identified positive cases out of all actual positive cases. It is used to evaluate the model's performance in detecting positive cases, i.e., the model's ability to capture actual positive instances.