## 2 Literature  
An adequate number of studies have been reported regarding the automatic analysis of SPECT MPI images in CAD diagnosis utilizing DL. In particular, [PERSON] et al., in [19], developed a CNN to estimate obstructive CAD. The dataset included 1160 SPECT MPI polar map cases without known CAD, in semi-upright and supine positions, and stress demonstrations without predefined coronary territories. Furthermore, sex was added as information between the fully connected layers. The classification was validated by utilizing a novel leave-one-center-out cross-validation procedure with four centers, which is equivalent to external validation. The proposed model was compared against the standard quantitative method cTPD (total perfusion deficit), and it emerged that deep CNN outperformed cTPD. More specifically, DL and cTPD achieved AUCs of 0.81% and 0.78% per patient, and 0.77% and 0.73% per vessel, accordingly. In addition, [PERSON] et al., in [20], explored the capabilities of CNN and TPD to successfully predict obstructive CAD. A total of 1638 patients without known CAD were included in stress SPECT MPI polar map representations. Moreover, the information about sex was added as an extra reference to the CAD's characteristics. The results demonstrated that the CNN achieved higher sensitivity than TPD, which was 82.3% and 79.8% per patient and 69.8% and 64.4% per vessel for CNN and TPD, respectively. The proposed model underwent a stratified 10-fold cross-validation procedure to evaluate the prediction. [PERSON] et al., in [21], investigated the prediction of abnormalities regarding CAD with the development of CNN. A total of 3318 images of stress polar maps were included, with patients in a supine position. A stratified five-fold cross-validation procedure, including additional rest scans, was used to evaluate the model. An expert reader labeled the images for classification purposes. Furthermore, data augmentation was used to reduce over-fitting and achieve generalizability. The results demonstrated that by adding rest perfusion maps, AUC improved, achieving 0.845 against 0.827, which was only observed in the case of the stress images. [PERSON] et al., in [22], explored the capabilities of CNNs to diagnose CAD automatically in a two-class classification problem. A total of 513 patients were included in the stress and rest representation, and the possible outputs were normal and abnormal. The data augmentation technique was utilized to increase the number of patients. Regardless of the small size of the dataset, the authors managed to extract magnificent values for AUC, such as 93.77%, with an accuracy of 90.2075%. In [5], [PERSON] et al. targeted the implementation of a CNN to automatically diagnose early signs of CAD (infarction or ischemia) utilizing SPECT MPI data. A total of 224 patients were included in the stress and rest representation. Data augmentation was also used to increase the size of the dataset. The authors implemented an RGB-CNN model and compared the results against a robust technique, which was transfer learning, employing VGG16, DenseNet, MobileNet, and InceptionV3 as pre-trained networks for the classification of images as normal or abnormal. The extracted results demonstrated the model's great future potential, with an accuracy of 93.47% \(\pm\) 2.81% and an AUC score of 0.936. [PERSON] et al., in [23], focused on using CNNs to categorize polar maps into normal and abnormal. This research consisted of 216 patient cases in a stress and rest demonstration, wherein both attenuation-corrected (AC) and non-corrected (NAC) polar maps were included. Concerning the small dataset, the authors followed two methodologies. The first one was transfer learning and, more specifically, VGG-16, which is widely utilized in image classification tasks. The second methodology applied data augmentation to increase the number of training images. The evaluation of VGG-16 was accomplished through 10-fold cross-validation.