# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 4 Experiments
### Experiment Design  
The training regimen for the model was meticulously crafted, involving numerous epochs at a carefully determined learning rate and batch size, selected to optimize the learning curve and prevent overfitting. The specific hyperparameters included:  
* Epochs: 20, to ensure adequate exposure to the training data without overfitting. * Batch Size: 16, balancing the computational load and the granularity of the gradient updates. * GPU: RTX 3060
* Learning Rate: 0.001, starting conservatively, to ensure comprehensive convergence over training epochs. * Optimizer: SGD, chosen for its adaptive learning rate capabilities, helping to fine-tune the model's weights effectively. * Loss: Cross-entropy loss. * Activation function: ReLU. * Convolution kernal: \(3\times 3\times 3\). * Pooling layer: Max pooling \(2\times 2\times 2\). * Loss: Cross-entropy. * Ratio of training/test: 0.8/0.2. This study systematically explored the impact of various augmentation strategies on the performance of medical image classification. Initially, we trained the model using the original dataset without any preprocessing. The results were initially promising, as after 20 epochs the model achieved an accuracy rate exceeding 99.8%. However, a deeper analysis revealed a significant issue: while the negative samples were classified with 100% accuracy, the classification of positive samples was entirely incorrect. In other words, the trained model defaulted to classifying all samples as negative, to minimize training loss. This behavior made sense from the model's perspective, due to the overwhelming imbalance in the dataset, where there was approximately one positive sample for every 400 samples, leading to inevitable overfitting. Augmenting the data could eliminate the imbalance in the dataset, compelling the model to learn the underlying patterns rather than merely memorizing the results. This approach sought to address the skewed distribution of classes and encourage a more robust learning process. Both geometric and photometric augmentation techniques were implemented, separately combining them to assess their respective contributions to enhancing classification accuracy. The specifics of these combinations are detailed in Tables 1 and 2. Figures 2 and 3 illustrate a positive instance and the augmented versions with different combinations of augmentation techniques. Additionally, the establishment of a baseline by training the model without any augmentation methods allowed us to measure the incremental benefit provided by each augmentation approach. This comparative analysis helped us to not only gauge the effectiveness of individual methods but also to evaluate their synergistic effects when combined. To rigorously assess the performance of each augmentation method, the calculations of various metrics were carried out. These metrics served as critical indicators of classification success, enabling us to identify the most effective techniques for improving model accuracy in medical image processing. By comparing these metrics across different augmentation scenarios, this work aimed to determine the most suitable strategies for significantly enhancing model performance. This approach ensures that one not only adopts the most effective augmentation methods but also that one tailors metric selection to best reflect the nuanced improvements these methods bring to complex medical imaging tasks. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Idx** & **Flip** & **Shift** & **Scale** & **Rot. ** & **Noise** & **Idx** & **Flip** & **Shift** & **Scale** & **Rot. ** & **Noise** \\ \hline
1 & \(\surd\) & & & & 17 & \(\surd\) & & \(\surd\) & \(\surd\) & \\ \hline
2 & & \(\surd\) & & & 18 & \(\surd\) & & & \(\surd\) & \(\surd\) \\ \hline
3 & & & \(\surd\) & & 19 &  
Figure 2: Augmented samples with geometric techniques. Figure 3: Augmented samples with photometric techniques. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Idx** & **Brt. ** & **Conts. ** & **Noise** & **Blur** & **Idx** & **Brt. ** & **Conts.