# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 5 Results and Discussion  
Table 3 presents a comprehensive overview of the experimental outcomes, arrayed according to the augmentation combinations detailed in Tables 1 and 2. The results reveal significant disparities across various augmentation strategies, indicating that the choice of metric profoundly influences classifier ranking. Metrics such as Accuracy, \(F_{1}\) Score, and the MCC demonstrated favorable outcomes with noise injection, which notably enhanced the True Negative Rate, suggesting improved performance under these metrics. Despite these gains, a critical observation is the low True Positive Rate, which indicates a substantial number of real nodules were mistakenly classified as non-nodules. This misclassification poses a severe risk in medical contexts where the early detection of nodules is crucial for timely and effective cancer diagnosis and treatment. Note that the metrics for experiment index 47, which did not utilize any augmentation techniques, indicate that the model tended to incorrectly classify all samples as non-nodules, a bias influenced by the overwhelming majority of non-nodule samples in the unbalanced dataset. To provide a clearer understanding, the results were further dissected based on individual metrics, with a specific focus on highlighting the top five performers for each metric in subsequent analyses. Figure 5: Training dynamics with image augmentation: (a) Correct/all. (b) Correct/neg. (c) Correct/pos. (d) Loss/all. (e) Loss/neg. (f) Loss/pos. this analysis suggests that accuracy alone is an insufficient metric for assessing classifier performance in this medical context. Table 5 displays the \(F_{1}\) scores in descending order, yet the recall values fluctuated significantly, ranging from 0.5074 to 0.7348. Although this shows some variability in performance, the recall rates remained lower than desirable for a screening project. This leads to the conclusion that while the \(F_{1}\) score considers both recall and precision, it does not reliably identify the best classifiers for this task, as it fails to consistently prioritize the high recall necessary for effective medical screening. Table 6, which ranks classifiers by their \(F_{2}\) scores, shows a significant improvement in recall compared to the values presented in Table 5, reaching levels that are more acceptable for a medical screening project. However, it is important to note that there was a substantial decrease in precision; a considerable portion of the predicted positive samples were actually negatives. Notably, as the \(F_{2}\) score decreased, recall diminished smoothly, suggesting that the \(F_{2}\) score is a useful indicator in this context, as it effectively balances the trade-off between recall and precision, prioritizing recall slightly more due to its higher weight in the \(F_{2}\) calculation. Table 7 identifies the most effective model based on recall, which demonstrates a trade-off, sacrificing precision to enhance recall. This model successfully classified over 96% of the nodules, albeit with low precision, indicating that only about one-sixth of the classified \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Index** & **Accuracy** & \(F_{1}\) **Score** & \(F_{2}\) **Score** & \(F_{3}\) **Score** & **MCC** & **Recall** & **Precision** \\ \hline
4 & 0.9955 & 0.4989 & **0.6762** & 0.8383 & 0.5539 & 0.8895 & 0.3467 \\ \hline
22 & 0.996 & 0.5138 & **0.6746** & 0.8123 & 0.5588 & 0.854 & 0.2488 \\ \hline
10 & 0.9963 & 0.5265 & **0.6722** & 0.7909 & 0.5636 & 0.8258 & 0.3864 \\ \hline
14 & 0.9974 & 0.5796 & **0.6634** & 0.7198 & 0.592 & 0.7348 & 0.4785 \\ \hline
2 & 0.9968 & 0.5376 & **0.6541** & 0.737 & 0.5616 & 0.7611 & 0.4156 \\ \hline \hline \end{tabular} Note: The table is ranked based on the bold values.  
\end{table} 
Table 6: Module prediction rankings based on \(F_{2}\) score.