# Ensemble Learning-Based Coronary Artery Disease Detection Using Computer Tomography Images
## 4 Discussion  
In this study, the authors developed a CAD detection model using the EL approach. Image preprocessing techniques were applied to overcome the image noise and artifacts. These techniques were employed to support the MobileNet V3 model to extract meaningful features. The 3D-U-Net model was used to generate the straightened coronary artery images. The artery straightening process was employed to produce the vertically stacked artery images. These images are crucial in identifying normal and abnormal images. The authors addressed the class imbalances using multiple data augmentation techniques. CatBoost and LightGBM models were used to generate the outcome using the image features. Finally, the outcomes were assembled using the RF model. The generalization of the CAD detection model was performed using two benchmark datasets. Baseline models were compared with the proposed model. Comparative analysis outcomes are presented in Tables 3 and 4. The recommended EL-based classification model obtained an exceptional Acc of 99.7 and 99.6 and F1 of 98.9 and 98.7 for datasets 1 and 2, respectively. The proposed EL-based CAD detection model obtained outstanding results in datasets 1 and 2. Compared to the existing models, the proposed model produced a higher level of accuracy. By assembling the predictions of the CatBoost and LightGBM models, the overall accuracy of CAD detection is improved. The suggested model outperformed the baseline models. In addition, the outcome of the uncertainty analysis is listed in Table 5. It revealed the reliability of the proposed model's results. The recommended artery straightening process assisted the model in detecting the crucial features needed to identify CAD. Finally, Table  
environment. A dedicated hyperparameter tuning was required to enhance the EfficientNet B7 model's efficiency. The MobileNet V3 model is a lightweight feature extraction model. An additional training phase and image preprocessing were required to detect CAD effectively. In contrast, the proposed model followed the EL approach for image classification. It required a limited amount of resources to generate the outcome. [PERSON] et al. [46] introduced an image preprocessing technique to enhance the quality of the CCTA images. They employed multiple filter techniques to support the DL models in detecting CAD. The proposed CAD detection model has used Frangi and Sato filters to overcome the challenges in classifying the CCTA images. The findings revealed that the suggested image preprocessing techniques have improved the proposed model's performance. The studies [47, 48, 49, 50] proposed a method for detecting CAD markers from the CCTA images. They applied image segmentation and pixel enhancement techniques to enhance the CCTA image quality. Likewise, the proposed model used the FBP algorithm for CCTA image reconstruction. The exceptional performance of the recommended model represents the significance of the FBP algorithm. The studies [51, 52, 53] discussed the importance of Bayesian inference models in decoding anomalous diffusion using CT images. Similarly, the proposed model employed feature engineering based on the MobileNet V3 model for classifying the normal and abnormal classes by identifying the intricate patterns of CAD. The suggested approach can automatically evaluate CT scans to detect CAD symptoms, assisting medical personnel with the initial screening procedure. The recommended CAD detection approach can rapidly analyze medical images to minimize the CCTA image interpretation period. With the help of the suggested model, it is possible to quantify the degree of coronary artery blockages and their severity. This information can assist physicians in determining the appropriate medical intervention, including angioplasty and coronary artery bypass grafting. Prioritizing patients by irregularity severity can optimize healthcare resource allocation.