## 1 Introduction
### Related Work
The proposed model agreed 89.3% with the human observer for rest test polar maps and 91.1% for the stress test polar maps. For localisation performed on a fine 17-segment division of the polar map, the agreement was 83.1% with the human observer. These findings indicate the model's potential in aiding CAD diagnosis and its ability to accurately identify pathological segments in cardiac images. [PERSON] et al. [27] developed a classification model for SPECT images to identify perfusion abnormalities, such as ischemia and infarction. The researchers investigated two different models. The first model was a deep learning (DL)-based approach that utilised state-of-the-art CNNs and fully connected support vector machine (SVM) layers to classify the deep-extracted image features. The second model involved image processing techniques, including segmentation, feature extraction, and colour thresholding, applied to the segmented parts of each SPECT slice. This method extracted five predefined image features that were classified using a rule-based algorithm. In terms of performance, the integrated CNN-SVM model achieved 92% accuracy, 84% sensitivity, and 100% specificity, based on the visual assessments conducted by experts. On the other hand, the knowledge-based classification attained 93% accuracy, 100% sensitivity, and 86% specificity. These metrics were reported on a test dataset that included 17% of the total samples. The results demonstrated the potential of both DL-based and knowledge-based approaches in accurately classifying SPECT images and detecting perfusion abnormalities, with each model showing strengths in different performance aspects. [PERSON] et al. [28] demonstrated a DL approach for automatically diagnosing myocardial perfusion abnormalities using stress MPI profile maps as the input, considering both abnormal and normal cases. The study included a substantial dataset of 37,243 patients who underwent stress-only and stress/rest SPECT MPI examinations using three SPECT/CT cameras. The DL model utilised the ResNet-34 architecture for feature extraction. In addition to the MPI data, six extra features, including gender, BMI, length, stress type, radiotracer, and the option of including or not including the attenuation correction, were incorporated into the model. The results were compared against the conventional quantitative perfusion defect size (DS) method. The DL model achieved an impressive AUC of 0.87, outperforming the DS method. Furthermore, the proposed network demonstrated robustness to variations in image acquisition devices, achieving 82% and 84% accuracy across all scanners. Notably, the model exhibited even greater performance in female participants, reaching an accuracy of 87%. These findings highlight the effectiveness and generalisability of the DL-based approach for diagnosing myocardial perfusion abnormalities, presenting a promising tool for improving cardiovascular diagnosis and patient care. [PERSON] et al. [29] acquired Polar Maps under stress, and rest conditions were used to diagnose CAD by employing the pretrained VGG16 model. The research involved 216 participants, and both attenuation correction (AC) and non-attenuation correction (NAC) Polar Map images were merged into a single image per patient. With reference to the findings from invasive coronary angiography, the VGG16 model achieved an accuracy of 74.53%, a sensitivity of 75.00%, and a specificity of 73.43%. In comparison, experienced nuclear medicine physicians' interpretations of the MPI yielded an accuracy of 75.00%, a sensitivity of 76.97%, and a specificity of 70.31%. The accuracy of a semi-quantitative polar map analysis using AC and NAC techniques was comparatively lower at 66.20% and 64.81%, respectively. Additionally, the VGG16 model demonstrated a robustness to variations in image acquisition devices. These results suggest the potential of the DL-based approach for CAD diagnosis, showing comparable performances to experienced physicians' interpretations and outperforming the traditional semi-quantitative methods.