###### Abstract  
Focusing on coronary artery disease (CAD) patients, this research paper addresses the problem of automatic diagnosis of ischemia or infarction using single-photon emission computed tomography (SPECT) (Siemens Symbaia S Series) myocardial perfusion imaging (MPI) scans and investigates the capabilities of deep learning and convolutional neural networks. Considering the wide applicability of deep learning in medical image classification, a robust CNN model whose architecture was previously determined in nuclear image analysis is introduced to recognize myocardial perfusion images by extracting the insightful features of an image and use them to classify it correctly. In addition, a deep learning classification approach using transfer learning is implemented to classify cardiovascular images as normal or abnormal (ischemia or infarction) from SPECT MPI scans. The present work is differentiated from other studies in nuclear cardiology as it utilizes SPECT MPI images. To address the two-class classification problem of CAD diagnosis, achieving adequate accuracy, simple, fast and efficient CNN architectures were built based on a CNN exploration process. They were then employed to identify the category of CAD diagnosis, presenting its generalization capabilities. The results revealed that the applied methods are sufficiently accurate and able to differentiate the infarction or ischemia from healthy patients (overall classification accuracy = 93.47% \(\pm\) 2.81%, AUC score = 0.936). To strengthen the findings of this study, the proposed deep learning approaches were compared with other popular state-of-the-art CNN architectures for the specific dataset. The prediction results show the efficacy of new deep learning architecture applied for CAD diagnosis using SPECT MPI scans over the existing ones in nuclear medicine. coronary artery disease; SPECT MPI scans; deep learning; convolutional neural networks; transfer learning; classification models +
Footnote †: journal: Medical Imaging  
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Footnote †: journal: Medical Imaging  
perfusion imaging (MPI), positron emission tomography (PET), and cardiovascular computed tomography (CT) have been utilized in clinical practice [8; 9; 10; 11; 12]. According to the EANM guidelines [13], radionuclide MPI with the contribution of SPECT imaging is a remarkably efficient technique regarding the CAD diagnosis [14; 15]. Myocardial perfusion scintigraphy (MPS) is a well-established, non-invasive imaging technique proven effective in diagnosing angina and myocardial infarction. Specifically, SPECT MPI depicts the information regarding the spreading of a radioactive compound within the heart in three dimensions and is considered the most frequently performed procedure in nuclear cardiology [16]. Among others, it is used for predicting future CAD events and identifying coronary artery disease severity. Regarding the detection of myocardial ischemia, MPS outperforms ECG in terms of accuracy [7; 17]. Furthermore, applying the MPS imaging method reduces the number of angiographies, offering proper treatment planning [18]. In the discipline of cardiovascular imaging using nuclear cardiology techniques, [PERSON] et al. explored the notion regarding the deployment of artificial intelligence (AI) based on modern machine learning, focusing on methods and computational models currently used [19]. The goal is to enhance diagnostic performance through complex image analysis and interpretation [20]. However, image interpretation is a demanding yet time-consuming task which relies mainly on physicians' experience [21]. On this basis, interpretation can be standardized with the contribution of CAD tools providing enhanced overall objectivity. The diagnostic accuracy is also improved, whereas the diagnostic time and healthcare costs are significantly reduced.