## 1 Introduction
### Related Work  
[PERSON] et al. [15] developed an RGB-CNN model to classify SPECT images as normal or ischemic, addressing a data scarcity issue with data augmentation. The model achieved impressive results, with 90.2% accuracy and a 93.77% AUC value, outperforming human reader interpretation as the ground truth. Despite the limited dataset, the model demonstrated remarkable predictive capabilities. The same research team [13] aimed to diagnose ischemia and/or infarction using CNNs with a dataset comprising 224 patients who underwent stress and rest SPECT tests, followed by invasive coronary angiography (ICA) 40 days later. They explored two deep learning techniques: implementing an RGB-CNN from scratch and employing transfer learning with pretrained models like VGG16, DenseNet, MobileNet, and InceptionV3 to classify images as normal or abnormal. Comparing the results to visual assessments by medical experts, the proposed CNN demonstrated significant abilities, achieving an overall accuracy of 93.48% \(\pm\) 2.81%. Once again, the CNN model showcased its potential in accurately diagnosing ischemia and infarction, even with a relatively limited dataset. This accuracy is significantly improved compared to the 90.2% initially obtained in [15]. [PERSON] et al. [16] investigated the capabilities of deep CNNs in distinguishing between normal and abnormal Polar Maps, using physician diagnoses as the reference. The dataset consisted of 3318 stress and rest Polar Maps, and data augmentation techniques were applied to expand the training dataset. The proposed deep learning model underwent thorough validation through a five-fold cross-validation procedure, achieving an AUC (Area Under the Curve) of 0.845. Notably, including rest perfusion, the maps led to a significant improvement in the DL model's AUC (0.845) compared to using only stress polar maps (0.827). [PERSON] et al. [17] aimed to explore the feasibility of automatically classifying polar maps as normal or abnormal using a custom RGB-CNN. The dataset comprised 314 polar maps in stress and rest representations, with AC (attenuation correction) and NAC (non-attenuation correction) formats. The RGB-CNN was trained using physician interpretations as the ground truth and was compared to the performance of the pre-trained VGG-16 network. The results showed that the RGB-CNN achieved an accuracy of 92.07%, while VGG-16 achieved 95.83%. Although the RGB-CNN's performance was slightly lower, it still competed effectively against robust state-of-the-art methods for polar map classification. In some research, deep learning-based results are compared against quantifiable metrics recommended by medical guidelines. These metrics provide objective and standardised measures to evaluate the performances of deep learning models in various medical tasks. By comparing the DL-based results with these established metrics, researchers can assess the effectiveness and reliability of the proposed models in clinical settings, ensuring they align with the best practices and recommendations provided by medical experts. For example, [PERSON] et al. [12] developed a DL model to identify CAD and evaluated its performance by comparing it with the total perfusion deficit (TPD) method. The dataset comprised 1160 patients and included raw upright and supine stress MyocardialPerfusion Imaging (MPI) polar maps. Both MPI and invasive coronary angiography (ICA) were conducted within a 6-month interval. The researchers employed a leave-one-centre-out approach with four different models for external validation. This validation method allowed them to assess the DL model's generalisation and performance across various medical centres, enhancing the reliability of the results. [PERSON] et al. [18] developed a CNN to identify CAD. The study included 1160 participants, and the data comprised semi-upright and supine stress Polar Map representations. Obstructive disease classification was evaluated using the leave-one-centre-out cross-validation technique with four centres.