# Ensemble Learning-Based Coronary Artery Disease Detection Using Computer Tomography Images
## 1 Introduction  
CAD is a form of cardiovascular disease that increases global mortality [1]. Hardening and narrowing coronary arteries reduces blood flow to the heart chambers [2, 3]. Early diagnosis can impede disease development and support healthcare centers in treating patients effectively [4]. CAD treatment options are based on the severity level of the disease [5]. Large-scale population screening using image-based detection technologies may be expensive, especially in developing countries [6, 7, 8, 9]. Due to these challenges, researchers consider non-invasive, cost-effective, rapid, and dependable methods to identify the disease in the initial stages [10]. In order to detect CAD, medical professionals use a variety of imaging techniques to inspect the heart, blood arteries, and tissues [11]. Stress testing can evaluate the cardiovascular system's capacity to resist physical stress by imaging and monitoring its electrical activity in response to exercise and drugs [12]. It may detect cardiac blood circulation issues. Nuclear stress tests measure blood circulation using radioactive tracers, whereas stress echocardiography measures heart function with ultrasound [13]. Acoustic waves are used in echocardiography to visualize the heart's anatomy and function. This approach may assist healthcare centers in identifying cardiac functionality, valve abnormalities, and regional wall motion anomalies [13]. Coronary angiography involves injecting contrastdye into the coronary arteries while performing X-ray imaging to measure blood flow and blockages [13]. It is used to identify coronary syndrome patients. Angiography is the standard intrusive procedure for determining CAD, which may harm the patient [14]. It allows cardiologists to examine blood artery issues, including stenosis, plaque, and obstruction [15]. Using their experience, cardiologists diagnose CAD and select treatment choices. This straightforward method requires optimal accuracy, objectivity, and consistency [16]. Evidence suggests automated systems may reduce human errors in CAD identification [17]. Coronary artery stenosis or atherosclerotic blockage may be observed using the high-resolution images [17]. A computed tomography (CT) scan is extremely adaptable. It can detect chronic cardiovascular diseases, cancers, and severe injuries [18]. CCTA is a non-invasive medical treatment that employs CT to provide comprehensive images of the coronary arteries [18]. It visualizes the anatomy of coronary arteries and plaque, stenosis, and other abnormalities. In addition, it is highly beneficial in ensuring the existence of CAD [19]. CT and CCTA are sophisticated imaging techniques to visualize intricate cross-sectional images [20]. These imaging techniques are crucial in assisting clinicians in diagnosing cardiac diseases. To assist cardiologists in detecting plaque and stenosis, blood vessels may be retrieved using image segmentation techniques [20]. These technologies improve prediction accuracy and treatments and provide follow-up care for individuals with cardiovascular diseases. Convolutional neural network (CNN) models are based on deep learning (DL) techniques [21]. The critical features of CAD can be extracted using the pre-trained CNN models. These models can analyze complex images and produce an optimal outcome. Transfer learning characteristics of DL techniques can be used in medical imaging for anomaly detection, size, and diagnosis [21]. Medical imaging applications employ the CNN models for identifying organs, tumors, and additional anatomical characteristics. CNN models can evaluate three-dimensional (3D) medical images, including CT scans and magnetic resonance imaging volumes [22]. 3D CNN models can capture spatial relationships, making them ideal for volumetric medical data analysis. DL-based medical image analysis models show potential in automated radiography, CT, and mammography anomaly identification and categorization.