## 1 Introduction  
Heart disease is the leading cause of death globally, and coronary artery disease (CAD) is the most common type of heart disease. Over 20,000 people die from cardiovascular diseases annually. Mortality rate due to CAD is higher than other kinds of cardiovascular diseases. CAD occurs when the blood vessels supplying the heart muscle become hardened and narrowed. Reduction in the prevalence, morbidity, and mortality related to CAD is an important issue of public health given the significant burden of diseases and contribution to total costs of health care. Early detection of CAD may improve life expectancy and quality of life by preventing myocardial infarction (MI) and heart failure.  
Noninvasive testing methods are used as first-line diagnostic and prognostic tools for patients presenting with chest pain or other symptoms of CAD to improve risk stratification of patients for CAD and to guide subsequent tests and interventions. Noninvasive diagnostics include functional tests such as exercise electrocardiography (ECG) [1], stress echocardiography [2], radionuclide myocardial perfusion imaging (MPI) using single photon emission computed tomography (SPECT), or positron emission tomography (PET) [3,4]. There are also noninvasive anatomic tests, including coronary CT angiography (CCTA) [5], coronary magnetic resonance angiography (MRA) [6], and coronary artery calcium scoring (CACS) [7].  
SPECT MPI is a widely used technique for the risk stratification and diagnosis of CAD. There are software packages available for quantification of relative perfusion data using normal databases to define the summed stress score (SSS) and total perfusion deficit (TPD), such as 4 DMSPECT (4 DM) [8], Emory Cardiac Toolbox (ECTb), and Cedars-Sinai Quantitative Perfusion SPECT (QPS) [9]. Automated quantification has provided similar overall diagnostic accuracy when compared to visual interpretation by expert observers for the detection of obstructive CAD.  
Recently developed MPI SPECT using a Cadmium Zinc Telluride (CZT) camera has proven highly efficient by reducing isotope dose and imaging time [10]. It maintains equal image quality and diagnostic accuracy as compared to MPI SPECT using a conventional gamma camera. Convolutional Neural Network (CNN) is a well-known deep learning architecture inspired by the natural visual perception mechanism of the living creatures. Due to the rapid application of deep learning in medicine [11]. Recently, nuclear cardiology has also started to adopt this technique. The deep learning approach includes an appropriate combination of features of abnormalities based on a large number of annotated images, and it differs from statistical approaches in software packages using regional count distribution based on means and deviations. By the literature review, there is only one published study addressing the application of CNN on the prediction of obstructive CAD, based on the classification of Technetium (Tc)-99m MPI images obtained from CZT cameras [12]. The purpose of this study is to propose a CNN-based model based on Thallium (TI)-201 MPI SPECT images to predict myocardial ischemia and to compare the diagnostic accuracy of this model against automated software package grading. This research has obtained the IRB approval from Chang Gung Medical Foundation, IRB No. 201801122B0C501.