# Ensemble Learning-Based Coronary Artery Disease Detection Using Computer Tomography Images
## 4 Discussion
The proposed model can evaluate massive datasets in a consistent and efficient manner, increasing the likelihood of identifying minor irregularities that may be missed in human evaluations. When compared with conventional approaches, the suggested CAD detection model can process medical image analysis efficiently. This efficiency enables healthcare practitioners to focus on interpretation and decision-making. The proposed model can quantify CAD markers, including vascular diameters, stenosis severity, and other quantitative treatment planning parameters. In contrast to subjective interpretations, it can offer a more objective and standardized evaluation, which may result in more consistent diagnoses. The experimental results indicated that there is no bias or overfitting in the model's outcome. Using the recommended model, healthcare centers maintain the users' data privacy. In resource-constrained contexts, the suggested CAD detection model can detect abnormalities in CCTA images. It maintained a trade-off between model complexity and interpretability. The proposed model can improve the diagnostic procedure without a substantial workforce or infrastructure. Healthcare facilities and cardiologists may greatly benefit from the proposed model for detecting CAD during the initial screening procedure. Within healthcare settings, patient data may produce a large amount of unpredictability. The authors trained the proposed model using benchmark datasets to handle such unpredictability. The model's generalizability is essential across a wide range of patient populations for reliable CAD diagnosis. Patients with a greater risk for CAD may be identified with the use of the suggested model. The proposed method can be beneficial in offering personalized treatments based on the patient's risk profile. Furthermore, the recommended CAD detection model can be updated or retrained to identify CAD in diverse patient populations. During the model development, the authors encountered a few challenges. The image noise and artifacts reduced the feature's quality. Multiple image preprocessing steps were required to improve the performance of the MobileNet V3 model. The artery straightening process was applied in order to generate vertically stacked artery images similar to dataset 2. The authors conducted various data augmentation techniques to improve the proposed model's performance. The RF-based classifier has demanded a fine-tuning process to streamline the classification process. The model was generalized to two datasets. However, it demands substantial training in order to generate an exceptional result in real-time applications. An additional training program is required to assist the cardiologist in making effective decisions. To ensure proper and effective deployment in healthcare practice, the developers should address model interpretability, ethical issues, and ongoing validation in diverse patient groups. The number of parameters and FLOPs depends on the dataset's size. However, the suggested model required limited resources to identify the normal and abnormal classes. The severity of CAD is based on factors including the age and lifestyle of the individual. To strengthen the decision-making capability of the proposed model, diverse datasets are required. There is a demand for effective collaboration between researchers and healthcare centers to use CCTA images while maintaining data privacy policies. This collaboration can reduce the data bias in CAD detection models. The proposed model can be improved using liquid neural network-based feature extraction. Multiple CNN models can be integrated with the proposed model to generate unique CAD features.