## 5 Results and Discussion
This suggests that there is potential for enhancing model performance by exploring larger and deeper architectures in future studies. Figure 18 presents the architecture of the large neural network employed in our study, while Figure 19 depicts the architectures of the medium and small neural networks that we also utilized. These architectural diagrams were generated using Netwon [49]. Figure 17: Training loss, validation accuracy and validation loss plots. Figure 18: Large neural network architecture for machine learning-based label quality assurance. Figure 20 displays several examples for which the model accurately predicts that the labels are good and the corresponding labels are, in fact, of high quality. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Size** & **Number of Learnable Parameters** & **Accuracy** \\ \hline small & 564,994 & 0.79 \\ \hline medium & 1,641,026 & 0.80 \\ \hline large & 23,285,570 & **0.82** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The relationship between the number of learnable parameters and the accuracy of a machine learning-based label quality assurance neural network. Figure 19: Smaller neural network architectures for machine learning-based label quality assurance. (**a**) Medium neural network architecture. (**b**) Small neural network architecture. Figure 21 presents a selection of examples for which the model correctly identifies the labels as bad and the corresponding labels are indeed of bad quality. Figure 22 showcases two instances where the model incorrectly predicts the labels as bad when they are actually good. These samples are likely outside the distribution of the _small set_ used for training the label quality assurance neural network. Figure 23 presents two examples for which the model erroneously predicts the labels as good when they are actually bad. Similar to the false negatives mentioned earlier, false-positive predictions are likely to be outside the distribution of the _small set_ used for training the label quality assurance neural network. Figure 21: Examples of true negatives. Figure 20: Examples of true positives. During training, only one of the error subtypes (A1, A2, A3, B1, or B2) is applied to the label if the sample is chosen to be negative. Further analysis involving different error types and subtypes and testing on specific combinations is conducted and presented in Table 4. The order of operations is important, and meaningful orders are shown. For instance, A1A2 means that A1 is first performed on the ground truth label, followed by A2 on the already modified ground truth label. There is an equal chance for a sample to be classified as positive or negative. If a sample is positive, labels are created within uncertainty regions, and if it is negative, a combination of error subtypes is performed on ground truth labels. The evaluation is performed using 1000 random samples from the test set 10 times for every combination. The mean and standard deviation for 10 runs per combination are shown. The label quality assurance neural network performs the worst on the A2 error subtype (distorting labels inside uncertainty regions to become outside of them), which is reasonable, as it can be challenging even for a human to determine if a bounding box with a good class is entirely inside the uncertainty region or not. The neural network best detects the B2 and B1B2 combinations (B1: adding new labels to ground truth classes outside any uncertainty region; B2: adding new labels to classes that do not belong to any ground truth classes, outside any uncertainty region). This outcome makes sense because these errors involve adding entirely new bounding boxes that do not belong to any uncertainty region. Figure 22: Examples of false negatives.