## 3 Results
### Visual Assessment  
Grad-CAM++ was implemented to visualise important areas on the Polar Maps, as suggested by the AFF-VGG19 network. The nuclear medicine specialists of the authors group performed a visual assessment regarding the highlighted areas on the Polar Maps.  
Based on the visual inspection, the model was reassessed. Figure 4 illustrates a sample of true-positive findings. Figure 5 illustrates true-negative, false-positive, and false-negative samples.  
The model revealed and highlighted important segments of the Polar Maps correctly. The suggested regions were relevant to the evidence of artery stenosis but of a greater spatial extent than the extent of the positive findings. Also, some maps were quite vague about the exact locations they dictated in the images and were considered incorrect. The inspection of 100 Polar Maps and the suggested regions of interest (the produced Grad-CAM++ heat maps) indicated a slight performance decrease, as Table 5 suggests.  
The 100 samples included 45 \(TP\) cases, 35 \(TN\) cases, 12 \(FP\) cases, and 8 \(FN\) cases. These metrics corresponded to a 0.8 accuracy in CAD identification. Of the 45 \(TP\) cases, 43 had meaningful suggested regions, 1 had an ambiguous region (too large), and 1 had an irrelevant suggested region.  
Of the 35 \(TN\) cases, 34 generated a meaningful heat map, and 1 an ambiguous (too large area). For the 12 \(FP\) cases, 4 heat maps located the suspected artery correctly, but the classification was wrong. In addition, eight of the twelve irrelevant suggestions were in the right locations, but the suggested class was not expected. The eight \(FN\) cases did not contain any highlighted areas. Therefore, these cases were considered to be irrelevant. Considering the number of meaningful explanations, the model's accuracy reached 77%, 3% less than the obtained accuracy without inspecting the suggested areas.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Study** & **Input** & **Reference** & **Explainability** & **Results** \\ \hline  & & & & Expert \\  & & & & **Accuracy**: 0.7 \\  & & & & **Sensitivity**: 0.89 \\
[30] & Polar Maps + Clinical & ICA & - & **Specificity**: 0.71 \\  & & & & Model \\  & & & & **Accuracy**: 0.78 \\  & & & & **Sensitivity**: 0.77 \\  & & & & **Specificity**: 0.79 \\ \hline  & & & & Per vessel \\
[20] & Polar maps + angina symptoms & & & **AUC**: 0.89 \\  & and age & & & Per patient \\  & & & & **AUC**: 0.95 \\ \hline  & & & & **Accuracy**: 0.7562 \\  & & & & **Sensitivity**: 0.7856 \\
[16] & Polar maps & Human reader & & Specificity : 0.7434 \\  & & & & **F1 score**: 0.6646 \\  & & & & **AUC**: 0.8450 \\ \hline  [43] & Polar maps & Human reader & & **Accuracy**: 0.92 \\ \hline  & & & & **AUC**: 0.76 \\
[24] & Stress rest polar maps combined & & & **AUC**: 0.73 \\  & with age, sex, and cardiac volumes & ICA & ✓ & (external dataset) \\ \hline  & & & & **Accuracy**: 0.8992 \\ This study & Stress and rest Polar Maps & Human reader & ✓ & **Sensitivity**: 0.8992 \\  & (AC and NAC.) & & & **Specificity**: 0.8755 \\ \hline  & & & & **Accuracy**: 0.78881 \\ This study & Stress and rest Polar Maps & & & **Sensitivity**: 0.8732 \\  & (AC and NAC.) & & & **Specificity**: 0.7216 \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._Figure 4: Grad-CAM++ examples of true-positive findings. Each row represents a different patient case, and each image pair represents the original image (**right**) and the Grad-CAM++ map (**right**).  
Figure 5: Grad-CAM++ examples of false-positive, true-negative, and false-negative findings. Each row represents a different patient case, and each image pair represents the original image (**right**) and the Grad-CAM++ map (**right**).