## 1 Introduction
### Related Studies
#### 1.2.1 CAD Diagnosis Using CNNs  
[PERSON] et al. [7] aimed to develop an explainable DL methodology for the automatic 3-class CAD classification problem (infarction, ischemia, normal) with Single Photon Emission Computed Tomography (SPECT) images. The dataset included 625 patients in stress and rest representation. Data augmentation was utilized to expand the dataset and achieve generalization. The model presented efficient results with 93.3% accuracy and 94.58% AUC, where k-fold cross-validation was used to ensure the model's reliability. Grad-CAM was also utilized as an explainable tool for the classification outcomes of the proposed model. [PERSON] et al. [18] developed RGB-CNN for two-class CAD classification with SPECT MPI images. A total of 224 patients were included in this study, and they were in stress and rest representation. Transfer learning was employed for comparison reasons against RGB-CNN with benchmark CNN models, like VGG-16, MobileNet, and InceptionV3. RGB-CNN outperformed these, with a 93.47 \(\pm\) 2.81% accuracy. [PERSON] et al. [19] explored three CNN architectures for 3-class CAD classification: RGB-CNN, VGG-16, and DenseNet-121. The dataset contained 647 CAD instances in SPECT-MPI format, and data augmentation was performed to increase the number of available instances. RGB-CNN, VGG-16, and DenseNet-121 achieved an accuracy of 91.86%, 88.54%, and 86.11%, respectively. Ten-fold cross-validation was also utilized. [PERSON] et al. [20] explored CNN to classify polar maps into normal and abnormal. The study consisted of 216 patient cases in stress and rest demonstrations, where the polar maps were in both attenuation-corrected (AC) and non-corrected (NAC) formats. VGG-16 was implemented with transfer learning, and 10-fold cross-validation was applied to estimate VGG-16's performance. Data augmentation was utilized as well. For comparison reasons, semi-quantitative methodologies were used and experts' analyses were performed, and VGG-16 performed best, with accuracy, sensitivity, and specificity values of 74.53%, 75%, and 3.43%, respectively. Semi-quantitative techniques attained 66.20% accuracy. [PERSON] et al. [21] proposed Graph CNN for the automatic classification of myocardial polar maps into normal and abnormal. A total of 946 instances were included in the dataset in stress and rest representations. For further evaluation of the model's performance, a 4-fold cross-validation was developed. Regarding the interpretability of the results, heatmaps were generated that illustrate the region of abnormality in each instance. The proposed model attained 89.9% on rest polar maps and 91.1% on stress polar maps. [PERSON] et al. [22] constructed a DL model for the accurate diagnosis of CAD in polar maps. For this research, 1160 patients without known CAD were included in upright and supine positions and only in stress demonstrations. Gender and BMI were also inserted into the dataset as clinical characteristics of patients. For the provision of explainability, Grad-CAM was applied to demonstrate the accountable regions for pathology. Concerning the result, the DL model achieved 82% sensitivity in men and 71% in women. For comparison reasons, the standard SSS (Summed Stress Score) was evaluated, yielding the following values: 75% and 71% for the upright Total Perfusion Deficit (U-TPD), 77% and 70% for the supine (S-TPD), and 73% and 65% in men and women, respectively. Based on the presented results, the study demonstrated significant variations in the diagnostic performance of DL compared to D-SPECT in predicting CAD in men and women. These differences may be attributed to the fact that men and women have different cardiac sizes, and certain factors differ between the genders. For the evaluation of results, leave-one-center-out external validation was applied. [PERSON] et al. [23] constructed an explainable DL network from scratch to diagnose obstructive CAD in SPECT MPI images. A total of 3578 patients were included with suspected CAD only in stress representation.