## 2 Literature
The extracted results were also compared against standard semi-quantitative methodologies and experts' analyses. The pre-trained VGG-16 network outperformed with an accuracy of 74.53%, sensitivity of 75%, and specificity of 73.43%, whereas the accuracy of the semi-quantitative analysis was 66.20%. [PERSON] et al., in [24], developed a hybrid method to automatically classify MPI polar maps, concerning the early diagnosis of CAD, in contrast to medical experts' diagnostic analyses. A total of 566 patients in a stress and rest representation were involved in this research, while the following clinical data were also added: gender, age symptoms, pre-disposing factors, and recurrent diseases. Data augmentation was used to apply variation to the images providing the model with generalizability. The authors developed a hybrid combination of InceptionV3 and random forest (RF) algorithms with the utilization of images and clinical data. The hybrid model with the InceptionV3-Random Forest approach extracted 79.15% accuracy and was similar to the expert's analysis, achieving a sensitivity of 77.36% and specificity of 79.25%. The results were evaluated with the 10-fold cross-validation technique. [PERSON] et al., in [6], aimed to investigate a DL approach to improve the diagnostic accuracy of CAD. A total of 37,243 patients in a stress mode were selected in this study, wherein count profile maps were extracted from SPECT MPI images. In addition, clinical data were included, such as gender, BMI, length, stress type, and radiotracer, along with the options of adding or not adding attenuation correction. A DL methodology with transfer learning was developed. More specifically, ResNet-34 was utilized and compared against a conventional quantitative perfusion defect size (DS). The DL prediction was evaluated utilizing five-fold cross-validation. The AUC results for the DL and DS methods were 0.872 \(\pm\) 0.002 and 0.838 \(\pm\) 0.003, respectively, the DL method accordingly showing better performance. [PERSON] et al., in [8], proposed two classification models, DL-based and knowledge-based, to identify perfusion abnormalities (infarction and/or ischemia). Concerning the first type, the authors developed transfer learning and a support vector machine (SVM) classifier. In regards to the second model, they focused on the expert readers' analysis to apply image processing techniques such as segmentation, color thresholding, and feature extraction. The dataset consisted of 192 patients in a stress and rest demonstration. The proposed models extracted results similar to the experts' analysis, providing an accuracy of 94% and 93%, sensitivity of 88% and 100%, and specificity of 100% and 86% for the DL-based and the knowledge-based models, respectively. [PERSON] et al. in [25] developed an ML algorithm to detect perfusion abnormalities on CAD images. A total of 1007 polar maps were included in the stress and rest representation, wherein each image was split into five vertical and five horizontal slices, and ten attributes were acquired. Moreover, data augmentation was used to increase the normal images of the dataset. The authors employed random forest as a classification algorithm and compared the extracted results with adaptive boosting (AB), gradient boosting (GB), and eXtreme gradient boosting (GB). The RF algorithm outperformed all, attaining an AUC of 0.853, accuracy of 0.938, precision of 0.968, and sensitivity of 0.963. [PERSON] et al., in [26], proposed an artificial neural network (ANN) to diagnose CAD, in contrast to a conventional quantitative approach concerning several metrics. The dataset included 1001 images in a stress and rest demonstration. Furthermore, patient data concerning CAD characteristics were included as additional information in the ANN algorithm. The clinical data were sex, age, weight, height, risk factors, coronary angiography results, and history of percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG).