## 4 Results
 & **Test Loss** & **AUC [CI 95\%]** & **Sens** & **Spec** \\ \hline
250 \(\times\) 250 & 16–32–64 & 128–128 & 92.7 & 0.22 & 89.45 & 0.24 & 0.91 [0.891–0.972] & 0.87 & 0.98 \\
250 \(\times\) 250 & 16–32–64–128 & 128–128 & 90.88 & 0.25 & 90.62  
of pixels in the classification results, representing the sensitivity of the CNN-based classification model to each pixel. The colormap Viridis, which was obtained from the OpenCV library, was used. For implementing the process of acquiring the correct results, Grad-CAM was initially fed with the extracted gradients of the images produced from the last convolutional layer of the model, generating the expected heatmaps. The heatmaps are two-dimensional and indicate the impact on the predicted output for each pixel value. In our study, Viridis colormap was selected. The high-impact value is displayed in yellow, whereas the low-level value is displayed in dark blue. Next, the overlay technique was applied, placing the produced heatmaps above the original image. This comparison can offer a better understanding to the nuclear experts. Based on the results, the generated heatmaps provide interpretation and compatibility to nuclear diagnoses concerning infarction and ischemic cases. It should be mentioned that Grad-CAM was applied in new images that were excluded from the training procedure to emulate a case with unseen data. In what follows, we provide some indicative visualization results of the Grad-CAM employment in several cases concerning infarction, ischemia, and normal. In particular, three cases were selected from each one of the three categories. RGB-CNN correctly classified these cases, while Grad-Cam was employed to visualize regions contributing to disease prediction on SPECT MPI images. In Figure 5a, according to the physicians' diagnosis (N.P. ), a large, fixed defect was present on all three slice orientations (SA-short axis, VLA-vertical long axis, and HLA-horizontal long axis images). In particular, this SPECT MPI scan showed a case of myocardial infarction with a fixed reduction in perfusion in the apex (see slice 9 in SA and slices 31-32 (row A) in VLA), extending to several apical segments to the mid-anterior and the mid-lateral wall--see slices 12-15 (row A) and slices 13-16 (row B) in SA, post-stress and at rest, respectively; slices 29-34 (row A and row B in VLA), slices 27-36 (row A), and 29-37 (row B) in HLA, at stress and rest, respectively. Before the classification process, the initial image was processed following the steps defined in Section 3.2.2 (see Figure 5b). Figure 4: Visualized results generated by Grad-CAM. By applying the proposed algorithm, Grad-CAM identifies the above regions of interest and colors them in bright yellow, providing a heatmap as illustrated in Figure 5c. Figure 5d represents the visualization result of SPECT MPI after placing the produced heatmaps above the processed image. A similar distribution of the yellow color on the heatmaps is observed in all examined infarction cases. In Appendix A, two more indicative infarction cases (cases B and C) are provided (see Figure A3), illustrating the yellow color distribution in the visualization regions representing the perfusion defects. In Figure 6a, according to the nuclear medicine physician's expertise (N.P), a medium-size reversible perfusion abnormality was diagnosed in the apex and the anteroseptal myocardium--see slices 10-14 (row A in SA), slices 30-33 (row A in VLA), and slices 31-35 (row A in HLA). By applying the algorithm, Grad-CAM identifies the segments with stress-induced hypo-perfusion in row A on all three axes in stress mode, which are marked in bright yellow (see Figure 6c). More specifically, after a thorough look at the visualized regions (Figure 6d) and comparing them to those of the initial image, it emerges that the algorithm sufficiently recognizes the post-stress defects. Figure 5: Case A. (**a**) Initial SPECT MPI image; (**b**) processed image; (**c**) produced heatmap; and (**d**) visualized image generated by Grad-CAM for infarction.