## 5 Discussion  
In this research study, we developed a fully automatic CNN-based method to detect any signs of infarction or ischemia in patients. The dataset included heterogeneous data of 625 patients. Among these data, 127 corresponded to infarction, 241 to ischemic, and 257 to normal, which nuclear experts had previously labeled for the current classification task. Given the small size of the population, we employed data augmentation to produce new images by applying different transformations to the current dataset, such as flipping and rescaling. In addition, we divided our dataset into three parts: validation, training, and testing. The validation dataset was used for fine-tuning the hyper-parameters, the training dataset for training the model, and the testing dataset for estimating its reliability. To determine the specifications of the proposed model, the authors performed an in-depth exploration analysis by examining various values for batch size, image size, number of nodes and layers for convolutional layer, and number of nodes for fully connected layers. Using the SPECT MPI images as our only input, we proposed a deep CNN with convolutional layers and two fully connected layers to enhance the accuracy of our corresponding three-class classification task. For the definition of the best model in terms of its performance, reliable metrics such as accuracy, loss, AUC with confidence internal, ROC curve, sensitivity, and specificity were utilized. The results demonstrated high efficiency and stability, achieving 93.33% accuracy and 94.58% AUC. Additionally, the authors conducted 10-fold cross-validation to further evaluate the model's stability and robustness. On  
Figure 7: Ischemia—Case B. (**a**) Initial image; (**b**) processed image; (**c**) heatmap; and (**d**) visualized results generated by Grad-CAM. the identical three-class data problem, the suggested model exceeded the performance of sophisticated deep learning networks (VGG-16 and ResNet-121), which only managed to attain lower levels of accuracy (88.54% and 86.11%, respectively), as demonstrated in [42]. In contrast to other traditional approaches, our RGB-CNN demonstrated superior performance for several reasons. First, it could extract optimum results despite the small dataset and without needing to employ other pre-trained networks that rely on existing datasets (e.g., ImageNet, which was trained in 1000 classes). Additionally, having performed an in-depth analysis of its parameters, the CNN model could avoid overfitting and achieve generalization. Finally, the proposed methodology includes simple architecture involving a small number of nodes, which provides minimized training time. Nevertheless, our research presents certain limitations. The proposed approach exclusively accepts images as input, despite physicians also considering additional clinical data such as age and sex to conclude an exact opinion about a patient's status. In our future considerations, we will seek to develop a hybrid method, which will use both images and clinical data as input to simulate the visual diagnosis of CAD fully. Concerning the clinical implications that unfold from applying the proposed DL-based approach, these entail the beneficial automatic clinical diagnosis of SPECT MPI images, which could prevent unwanted heart conditions such as ischemia and infarction. The CNN-based method can serve as a vital tool to assist medical experts in providing a precise diagnosis of SPECT MPI images and explicit treatment suggestions to patients suffering from CAD. On the other side, CNNs do not offer transparency and interpretability in their decisions, which is a critical throwback for their full integration into medical image analysis. Thus, doctors cannot rely on the provided predictions. CNNs are characterized by nature as black boxes since they do not supply details regarding their internal prediction process, leaving researchers to depend exclusively on the values of reliable metrics.