## 1 Introduction
### Related Work
All validated predictions were combined to avoid single-centre bias. Notably, the CNN model diagnosed without relying on predefined coronary territories. The CNN's performance was compared against the combined perfusion quantification using TPD, achieving a sensitivity of 84.8%, surpassing the 82.6% sensitivity obtained with clinical reading. In a subsequent study, [PERSON] et al. [19] compared the automatic diagnosis of CAD using SPECT image inputs with a deep CNN against the TPD method. They examined 1638 patients without known CAD who underwent invasive coronary angiography within six months of MPI. The dataset included raw and quantitative polar maps in stress representations only. To evaluate their proposed deep learning model, they employed a stratified 10-fold cross-validation procedure. The AUC score for disease prediction using the DL model was superior to TPD (per patient: 0.80 vs. 0.78; per vessel: 0.76 vs. 0.73). When the DL threshold was set to the same specificity as TPD, the per-patient sensitivity improved from 79.8% (TPD) to 82.3%, and the per-vessel sensitivity improved from 64.4% (TPD) to 69.8%. In addition to differentiating between normal and abnormal subjects, certain studies focused on region-based classification. Instead of considering the overall classification of the entire image, region-based classification involves identifying and classifying specific regions or regions of interest within the image. This approach allows researchers to gain more detailed insights into the particular areas or regions that may be affected by certain conditions or diseases. [PERSON] et al. [20] developed a CNN to predict obstructive coronary artery disease in the left anterior artery, left circumflex artery, and right coronary artery using SPECT Polar Maps. The research involved 588 patients, and clinical data, including angina symptoms and age, were incorporated into the analysis. The proposed CNN framework demonstrated promising results, achieving an average AUC of 0.89 per vessel and an impressive 0.95 per patient, with the invasive coronary angiography (ICA) findings used as the reference standard. To provide visual insights into the basis of the predictions, the researchers employed gradient-weighted class activation mapping (Grad-CAM) to highlight the regions influencing the model's output. Notably, the authors identified sex differences in the diagnostic performance of the deep learning model for predicting obstructive CAD from D-SPECT, with the CNN outperforming the visual and TPD methods in men but not in women. This finding underscores the importance of considering potential sex-specific variations when employing DL models for a CAD diagnosis. There is a growing trend in research towards proposing explainable deep learning (DL)-based methods that perform image classification and provide insights into the suggested areas of interest, where the model bases its predictions. These methods aim to enhance the interpretability and transparency of DL models, addressing the \"black box\" nature of traditional deep learning algorithms. By incorporating explainable techniques, researchers seek to inform users about the specific regions or features in the input images that contribute to the model's decision-making process. This builds trust in the model's predictions and allows domain experts, such as doctors in medical imaging applications, to better understand and validate the results. Explainable DL methods are becoming increasingly important in various fields, including healthcare, where accurate and interpretable predictions are critical for decision-making and patient care. [PERSON] et al. [21] developed a DL model to enhance the diagnostic accuracy of CAD and aid in physical interpretation. The dataset consisted of 240 patients who underwent MPI examinations, with invasive coronary angiography used as the reference standard. The results showed that, when human readers used the DL's predictions, they achieved an AUC of 0.779, whereas their interpretation without DL assistance reached an AUC of 0.747.