# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 5 Results and Discussion
### Augmentation Method Analysis  
Table 9 highlights the performance of the top five augmentation combinations, revealing a distinct preference for geometric augmentations. This preference correlates with the characteristics of the LUNA dataset, where CT scans are aligned in such a way that photometric variations provide minimal additional information. Geometric augmentations effectively mimic the variations in human organs and tissues, significantly enhancing the model's capability to process CT scans. Notably, the best-performing augmentation combination excluded rotation, while the next four highest-ranked combinations all included rotation, suggesting its individual contribution to model performance. This intriguing observation warrants further investigation in future research, to better understand the role of rotation and other geometric transformations in enhancing diagnostic accuracy. In Figure 6, it is evident that the best augmentation combinations outperformed other strategies, in terms of the \(F_{5}\) score, and that the highest \(F_{5}\) scores consistently correlated with a high recall rate. Because the \(F_{5}\) score aims to balance recall and precision, its specific ability to do so effectively makes it more suitable than other metrics for evaluating performance in this context. The results indicate a clear preference for geometric augmentations in enhancing model performance. Among these, the most effective combination included flip, shift, scale, and noise, followed by other primarily geometric strategies. This outcome can be attributed to several key factors inherent to the nature of medical imaging and the specific challenges posed by lung nodule detection. Firstly, the superior performance of the combination involving flip, shift, scale, and noise can be linked to their collective ability \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Index** & **Accuracy** & \(F_{1}\) **Score** & \(F_{2}\) **Score** & \(F_{5}\) **Score** & **MCC** & **Recall** & **Precision** \\ \hline
28 & 0.9902 & 0.3322 & 0.5468 & **0.8428** & 0.4381 & 0.9693 & 0.2004 \\ \hline
11 & 0.9921 & 0.3728 & 0.5837 & **0.841** & 0.4653 & 0.9401 & 0.2325 \\ \hline
4 & 0.9955 & 0.4989 & 0.6762 & **0.8383** & 0.5539 & 0.8895 & 0.3467 \\ \hline
15 & 0.9948 & 0.4589 & 0.6494 & **0.8366** & 0.5245 & 0.8984 & 0.3082 \\ \hline
13 & 0.9929 & 0.3939 & 0.5989 & **0.8357** & 0.4787 & 0.9237 & 0.2503 \\ \hline \hline \end{tabular} Note: The table is ranked based on the bold values.  
\end{table} 
Table 7: Nodule prediction rankings based on \(F_{5}\) score. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Index** & **Accuracy** & \(F_{1}\) **Score** & \(F_{2}\) **Score** & \(F_{5}\) **Score** & **MCC** & **Recall** & **Precision** \\ \hline
14 & 0.9974 & 0.5796 & 0.6634 & 0.7198 & **0.592** & 73.48 & 0.4785 \\ \hline
5 & 0.9981 & 0.5855 & 0.5516 & 0.536 & **0.59** & 53.25 & 0.6502 \\ \hline
12 & 0.997 & 0.5468 & 0.6427 & 0.7108 & **0.5639** & 72.94 & 0.4373 \\ \hline
10 & 0.9963 & 0.5265 & 0.6722 & 0.7909 & **0.5636** & 82.58 & 0.3864 \\ \hline
2 & 0.9968 & 0.5376 & 0.6541 & 0.737 & **0.5616** & 76.11 & 0.4156 \\ \hline \hline \end{tabular} Note: The table is ranked based on the bold values.  
\end{table} 
Table 8: Nodule prediction rankings based on MCC score. to mimic real-world variations in lung scans. Flipping the images and shifting the images simulate changes in patient positioning and orientation relative to the imaging equipment. This is crucial because slight variations in how a patient is positioned can significantly alter the appearance of nodules. Scaling addresses the variation in nodule size, which can be affected by the distance of the nodules from the imaging device, among other factors. Noise introduction, while often considered a photometric augmentation, in this context helps model robustness by simulating slight imperfections in imaging technology, such as electronic noise or sensor artifacts.