## 4 Proposed Methodology
### Ensemble Learning
\[\text{The Total Accuracy}=\frac{\text{Total Number of Correctly Detection}}{\text{Total Number of images}} \tag{13}\]

The confusion matrix is considered another performance metric that is used to evaluate the proposed model. It explains the expected classifications are either positive (the patient \begin{table}
\begin{tabular}{c c c} \hline \hline
**vzrad2 Dataset (V4)** & **vzrad2 Dataset (V5)** & **vzrad2 Dataset (V6)** \\ \hline Train Set 62\% by 5773 Images & Train Set 56\% by 3617 Images & Train Set 57\% by 4774 Images \\ Valid Set 21\% by 1956 Images & Valid Set 25\% by 1612 Images & Valid Set 25\% by 2074 Images \\ Test Set 16\% by 1519 Images & Test Set 20\% by 1268 Images & Test Set 19\% by 1581 Images \\ \hline
**Preprocessing:** & **Preprocessing:** & **Preprocessing:** \\ \(\bullet\) & Auto-Orient: Applied & \(\bullet\) \\ \(\bullet\) & Resize: Stretch to 640°640 & \(\bullet\) \\ \(\bullet\) & Modify Classes: 14 remapped, & \(\bullet\) \\ \(\bullet\) & 33 dropped & 38 dropped \\ \(\bullet\) & Filter Null: Require all images to & \(\bullet\) \\ \(\bullet\) & \(\bullet\) & Filter Null: Require all images to \\ \(\bullet\) & \(\bullet\) & contain annotations \\ \hline
**Augmentations:** & **Augmentations:** & **Augmentations:** \\ \(\bullet\) & Outputs per training example: 3 & \(\bullet\) \\ \(\bullet\) & Grayscale: Apply to 25\% of images & \(\bullet\) \\ \(\bullet\) & Noise: Up to 5\% of pixels & \(\bullet\) \\ \(\bullet\) & Mosaic: Applied & \(\bullet\) \\ \(\bullet\) & Bounding Box: & \(\bullet\) \\ \(\bullet\) & Rotation: Between \(-15\lx@math@degree\) and \(+15\lx@math@degree\) & \(\bullet\) \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline
**Vzrad2 Dataset (V4)** & **vzrad2 Dataset (V5)** & **vzrad2 Dataset (V6)** \\ \hline Train Set 62\% by 5773 Images & Train Set 56\% by 3617 Images & Train Set 57\% by 4774 Images \\ Valid Set 21\% by 1956 Images & Valid Set 25\% by 1612 Images & Valid Set 25\% by 2074 Images \\ Test Set 16\% by 1519 Images & Test Set 20\% by 1268 Images & Test Set 19\% by 1581 Images \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The versions of datasets used in the proposed model. is sick) or negative (the patient is healthy). The proposed model is run on 1000 patients, and the model predictions are entered. The recall metric known as sensitivity tells us how many of the sick patients our model incorrectly diagnosed as well [32]. It also shows how many times the model incorrectly diagnosed a sick patient as negative. In this paper detection recall is considered another metric that was calculated as in (14). \[\text{Detection Recall}=\frac{\text{Number of successful match}}{\text{Total Number of standard bounding box}} \tag{14}\]

Precision (also known as specificity) is the opposite of recall. It tells us how many of the healthy patients our model incorrectly diagnosed as sick. In other words, how many times did the model incorrectly diagnose a healthy patient as positive? Precision is calculated as in (15). \[\text{Detection Precision}=\frac{\text{Number of true positive}}{\text{Number of detected bounding box}} \tag{15}\]

F-score summarizes the performance of a classifier with a single metric that represents both recall and precision, as in (16). \[F\text{-score}=2\cdot\frac{\text{Detection Precision}\cdot\text{Detection Recall}}{\text{Detection Precision}+\text{Detection Recall}} \tag{16}\]

The mean average precision (mAP) metric means simply the average of AP of all classes. It helps to assess the balance between precision and recall for an object detection model across various probability thresholds. A higher mAP value indicates better performance of the object detection model, as in (17). \[mAP=\frac{1}{n}\sum_{k=1}^{k=n}APK \tag{17}\]

where \(n\) is the number of classes and \(APk\) is the average of the precision of class \(k\). In the proposed model, mAP50-95 is calculated. It refers to the mean Average Precision (mAP) calculated at multiple Intersection over Union (IoU) thresholds, ranging from 0.5 to 0.95 with a step size of 0.05.