## 3 Results
### Negative (Adversarial) Augmentations  
In Table 4, we find a comparison of several adversarial augmentation models based on metrics including Accuracy, Precision, Recall, and F1-Score. The model with noise level 05 had the highest performance across all metrics. As noise increased (i.e., Noise 10 and Noise 20), a corresponding decrease was observed in all performance metrics. This suggested that lower levels of noise improved the model's ability to generalize. In comparison, higher noise levels degraded the performance, likely due to interference with essential radiograph features. The models using cube techniques also showed varying levels of performance. Cube 2, for instance, performed better than Cube 3 and Cube 6 in all aspects. This could imply a potential optimal size or representation for the cube that best captured critical information. The models with no Region of Interest (ROI) performed poorly compared to others. The Noise 50 model recorded the lowest performance. In Figure 8, the Cube 3 model performed well when identifying KL0; however, as the Kellgren-Lawrence (KL) grades increased, this performance gradually diminished, culminating in a notable difficulty when classifying KL4. This suggested that while the model could easily distinguish KL0 from other classes, the higher grades posed more of a challenge. In contrast, the Cube 2 model showed a more even performance across all KL grades, with a gradual decrease in accuracy from KL0 to KL4. While the model also performed best on KL0 and worst on KL4, it demonstrated a more balanced confusion across different classes. The Cube 6 model continued the trend observed in Cube 3 and Cube 2, struggling with higher KL grades and decreasing performance from KL0 to KL4. Interestingly, it confused KL0 with KL1 and KL3 more than Cube 3 and Cube 2, which indicated its difficulty differentiating between these classes. Shifting to the Noise models, Noise 10 and Noise 05 displayed interesting behavior. They were fairly accurate when classifying the extreme KL grades (KL0 and KL4), but encountered difficulties with the intermediate classes. The Noise 20 model, like Cube 2, demonstrated an evenly distributed performance across the grades, but it performed slightly better on class KL4 than Cube 2. This could point to a better ability to differentiate between the features of the KL4 grade. The Noise 50 model was unique in its performance, showing a high confusion rate, particularly between KL0 and KL1, and KL1 and KL3. It also struggled with KL0, KL1, and KL2, yet performed reasonably well on KL4, indicating that this model found the lower and intermediate KL grades more challenging. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Noise 05 & 0.619 & 0.592 & 0.616 & 0.591 \\ Noise 10 & 0.591 & 0.572 & 0.601 & 0.574 \\ Noise 20 & 0.478 & 0.489 & 0.544 & 0.495 \\ Cube 2 & 0.483 & 0.469 & 0.502 & 0.458 \\ No ROI & 0.427 & 0.315 & 0.343 & 0.289 \\ Cube 3 & 0.407 & 0.347 & 0.406 & 0.299 \\ Cube 6 & 0.359 & 0.287 & 0.333 & 0.272 \\ No ROI Split & 0.349 & 0.294 & 0.371 & 0.263 \\ Noise 50 & 0.186 & 0.262 & 0.291 & 0.174 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics of the model on the test set using negative/adversarial base augmentations. Lastly, the No ROI model showed exceptional performance when classifying KL0, but it struggled to differentiate KL0 from KL2, and performed notably poorly on KL3. Similarly, the No ROI Split model showed a distinct performance pattern. It was particularly adept at identifying KL0 and KL4. In the No ROI model, the high score for the KL0 class (0.84) indicated that the model effectively identified patterns associated with KL0. However, there is an absence of the primary region of interest. Similarly, the No ROI Split model achieved a surprisingly high score of 0.75 for the KL4 class.