# Ensemble Learning-Based Coronary Artery Disease Detection Using Computer Tomography Images
## 2 Materials and Methods
### Image Acquisition  
The authors generalized the proposed model using two datasets. Dataset 1 consists of 1000 3D CCTA images [41]. The data were collected from the Guangdong Provincial People's Hospital, China, between April 2012 and December 2018. The images were captured using a Siemens 128-slice dual-source scanner. The size of CCTA images is 512 \(\times\) 512 \(\times\) (206-275) voxels with a planar resolution of 0.29-0.43 mm\({}^{2}\) and spacing of 0.25-0.45 mm. Dataset 2 encompasses 2364 CCTA images of 500 patients [42]. The images were represented in Mosaic Projection View (MPV) format, consisting of 18 unique views of vertically stacked straightened coronary arteries. Table 1 offers the attributes of datasets 1 and 2, respectively.  
Straightening arteries reduces vascular complexity, simplifying the feature extraction process [43]. Stacking straightened images generates cohesive input for training the DL models. Straightened artery images are stacked to minimize optical angle and tortuous vessel fluctuations. Using these characteristics, the CNN models classify the images. This consistency helps the model learn and generalize across scenarios [43]. Deep learning algorithms may require fewer layers and parameters to extract useful information from straightened artery images compared to complex, convoluted representations. This may boost model performance and training efficiency. In order to extract the straightened arteries from the 3D CCTA images, the authors follow the [PERSON] et al. [43] model. However, they used a three-dimensional U-Net model to improve the performance of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Normal** & **Abnormal** & **Total Number of Images** \\ \hline
1 & 503 & 497 & 1000 \\ \hline
2 & 1182 & 1182 & 2364 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Attributes of the datasets.  
Figure 1: The proposed CAD detection model.  
extraction process. The annotated 3D CCTA images are processed through the U-Net model in order to extract the normal and abnormal images. The centerline extraction method is used to deform the mean shape model to the vessel volume. It utilizes the coronary ostia and cardia chambers for centerline extraction. Figure 2 shows the recommended straightened artery extraction technique.  
Equation (1) shows the vessel extraction process.  
\[V_{i}=Centerline\_extraction(CCTA_{i},Co,Cc) \tag{1}\]  
where \(V_{i}\) is the vessel-centerline extraction, \(Co\) is the coronary ostia, and \(Cc\) is the cardiac chamber.  
The authors applied custom clamping logic to handle the variations in the coronary artery lengths. The empty frames were integrated with the arterial volumes to reshape the vessel borders. The authors used the weights of the 3D-U-Net model to process the coronary MPV volumes. A set of five convolutional layers with 16, 32, 64, 128, and 256 filters were used for constructing the CNN model. A 2 \(\times\) 2 \(\times\) 2 max pool and ReLu layers were used for handling the spatial invariance and model parameters. A flattened layer with a fully connected (FCN) layer generated normal and abnormal CCTA images.