## 4 Results and Discussion
### Four-Class to Two-Class Classification  
The obtained results reveal important insights into the performance of various classification approaches. One notable observation is the comparison between the two-class classification and the four-class classification converted to two-class classification. The two-class classification refers to the classification task where the model distinguishes between two specific classes (e.g., healthy and sick), while the four-class classification converted to two-class classification involves combining multiple classes into two broader categories. The results indicate that the four-class classification converted to two-class classification yields a significantly better performance than two-class classification. This suggests that the inclusion of additional classes in the training process provides valuable information that enhances the overall accuracy of the model. By training the model on a dataset consisting of multiple classes, it can capture a wider range of patterns, variances, and characteristics present in the data. This broader understanding and increased complexity of the model contribute to an improved classification accuracy when distinguishing between the two broader categories in the four-class classification converted to two-class classification scenario. The enhanced performance achieved with the four-class classification converted to two-class classification demonstrates the importance of considering a more comprehensive representation of the data during model training. By incorporating additional classes, the model can learn more nuanced and discriminative features, leading to better differentiation between the target categories. This finding highlights the value of leveraging the full range of available classes and their associated information when constructing classification models. The obtained results highlight the importance of carefully designing the classification task and selecting the appropriate representation of classes to achieve optimal performance. By leveraging the additional information provided by the four-class classification, the model gains a deeper understanding of the underlying data, resulting in improved accuracy when \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Validation** & **O\_4x1** & **A\_3\_1\_1** & **AW\_3\_1\_1** \\ \hline original dataset & 93.56\% & 88.88\% & 81.55\% \\ augmented dataset & 91.01\% & 93.91\% & 92.21\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The validation accuracies of three models with identical architectures were assessed. The first model, referred to as O_4x1, was trained on the original dataset, which consisted of a roughly equal number of examples per class. The second model, denoted as A_3\(1\)1_1, was trained on an upsampled dataset, while the third model, labeled as AW_3\(1\)1_1, utilized the upsampled dataset with additional class weights proportional to their respective proportions. distinguishing between broader categories. This underscores the significance of considering the relevance and inclusion of additional information in classification tasks. Furthermore, the findings also underscore the importance of selecting appropriate input features in classification models. In this case, the inclusion of velocity vectors as input features was found to be more effective compared to the use of a heatmap representation. This suggests that the choice of input features plays a critical role in capturing relevant patterns and characteristics for accurate classification. Moreover, the study highlights that the network's architecture and optimization techniques are crucial factors influencing the final performance. It is important to consider the interplay between the model's architecture, training algorithms, and the specific classification task at hand. Fine-tuning these components and exploring alternative approaches may further improve the accuracy and performance of the classification model.