# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 4 Results and Discussion of Selected Articles
### Analysis and Discussion  
Computer-aided diagnosis (CAD) systems are employed in the medical field to support doctors in their decision-making processes, but the design and adjustment of traditional CAD technologies have been very challenging. Today, artificial intelligence (AI) technology has been incorporated into these computer-driven diagnostic systems. Deep learning is one of its advantages, but its black box nature limits its applicability. Therefore, a plethora of explainable methods have been suggested to improve the transparency of deep learning, among which CAM has its merits. Firstly, CAM technology, as an explanatory tool, presents heatmaps in a way that intuitively shows the regions of interest in medical images that deep learning models focus on. It can assist in locating areas of pathology in medical images and also help improve model performance through observations of these areas. For example, [PERSON] [25] and colleagues developed a high-performance deep learning model based on CNN that can distinguish scaphoid fractures, the most common type of wrist bone fracture, in X-ray images. In this research, they used Grad-CAM technology to assist in locating the position and area of the fracture, achieving a classification accuracy of 95% in a binary setting. Rapid diagnosis and treatment are crucial for brain hemorrhage. [PERSON] [29] used an explainable \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Article Citation** & **Sensitivity** & **Specificity** & **F1-Score** & **AUC** & **IoU** \\ \hline ([PERSON] et al., 2023) [18] & 90 & / & / & / & / \\ ([PERSON] et al., 2023) [20] & / & / & / & / & 59.5 \\ ([PERSON] et al., 2021) [21] & 98 & / & / & 97.8 & / \\ ([PERSON] et al., 2023) [22] & 87.33 & / & 91.61 & / & / \\ ([PERSON] et al., 2022) [23] & / & / & 65 & 84 & / \\ ([PERSON] et al., 2023) [25] & 85 & 91 & / & 88 & / \\ ([PERSON] et al., 2021) [29] & 67 & 86 & / & / & / \\ ([PERSON] et al., 2022) [30] & / & 98 & 93 & / & / \\ ([PERSON] et al., 2022) [33] & / & / & / & 99 & / \\ ([PERSON] et al., 2022) [34] & / & / & / & 96 & / \\ ([PERSON] et al., 2021) [41] & 97.1 & / & 93.1 & / & / \\ ([PERSON] et al., 2021) [42] & / & / & 96.7 & / & / \\ ([PERSON] \& [PERSON], 2020) [44] & 95.8 & 97.1 & / & 98.6 & / \\ ([PERSON] et al., 2022) [45] & 97.7 & 92.6 & 95.7 & 95.1 & / \\ ([PERSON] et al., 2022) [47] & / & / & 96.44 & 99.11 & / \\ ([PERSON] et al., 2022) [48] & 87.93 \(\pm\) 1.47 & 80.65 \(\pm\) 3.59 & / & 91.89 \(\pm\) 01.15 & / \\ ([PERSON] \& [PERSON], 2023) [50] & 92.22 & 98.5 & 92.29 & / & / \\ ([PERSON] \& [PERSON], 2022) [51] & 95.65 & 96.2 & 95.91 & / & / \\ ([PERSON] et al., 2021) [54] & 99.69 & / & / & / & / \\ ([PERSON] et al., 2022) [59] & 91 & / & / & / & / \\ ([PERSON] et al., 2022) [60] & / & / & 82.1 & / & / \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics. deep learning model in their article to detect brain hemorrhages and their locations in images obtained from computed tomography (CT) scans. They examined images of normal brain scans as well as scans showing subarachnoid, intraventricular, subdural, epidural, and intraparenchymal hemorrhages. Using Grad-CAM for heatmap-like visualization explanations at the bleeding locations, they achieved an accuracy of 81% in predicting the location of hemorrhages in the medical images. Furthermore, [PERSON] [43] and colleagues employed electrocardiograms (ECG) for the classification of myocardial infarction (MI) in their article. They improved DenseNet and CNN models for classification and used Grad-CAM technology for the output of both models. This was done to observe and visualize specific ECG leads and waveform segments that had the most significant impact on the predictive decisions made by the models. The final classification result reached an accuracy of 95%. Furthermore, CAM technology can also serve as a tool to achieve validation, assessment, and comparison in model usage.