# Deep Neural Networks for HER2 Grading of Whole Slide Images with Subclasses Levels
## 5 Discussion  
This section presents a discussion of our findings, with a particular focus on analyzing the influence that different architectures and augmentation techniques have on performance. In addition to this, it provides an analysis of the strategy of dividing the classes that are the most uncertain in order to establish a distinct decision boundary. The preliminary tests conducted with DS0 have produced encouraging results and laid the groundwork for further comparisons with various configurations in the future. First, the process of augmentation begins with spatial transformations, which are then followed by the application of color transfer in order to improve the results. Through the utilization of this dual approach, the dataset is subjected to a greater degree of staining variation, which ultimately leads to an improvement in performance across all models operating within the 5-class classification. The ResNet and DenseNet models, which are the most complex, also demonstrate improved performance in the 7-class classification. This is a significant finding. The results of our experiments demonstrate that augmentation and balancing techniques are essential for achieving stable and consistent results across all models. This highlights the significance of balancing as an essential component for efficient learning. A reduction in the risks of overfitting is achieved through the utilization of these methodologies, which ultimately results in improved outcomes for classes that are underrepresented. It is important to highlight the robust and dependable nature of these techniques by mentioning the performance improvement that was observed simultaneously across all classes and models. Upon comparing confusion matrices with the traditional 5-class approach, it becomes clear that the innovative 7-class approach has a number of advantages that are readily apparent. The ambiguous grading decisions and treatment prognosis are significantly impacted as a result of this. According to the ResNet model that makes use of DS3 and the 7-class approach, the sensitivity for classes 2.5+ and 3+ is 95.2% and 96.1%, respectively. When these sensitivities are combined in the aggregated 3+ class, the overall sensitivity reaches 97%. DenseNet demonstrates a sensitivity of 93% for class 3+ when it is applied to DS2. Comparing the 7-classes approach aggregated to the 5-classes for class 2+, it reveals that the former exhibits a 3% greater sensitivity than the latter, using the same model. The ResNet model demonstrated a sensitivity of 88% when evaluated on a dataset with 5 classes. However, when the model was tested on a dataset with 7 classes and the results were aggregated to 5 classes, the sensitivity improved to 91%. The level of variation in class 1 is significantly higher, increasing by 11.5%. Thus, the sensitivity of class 1 when applied to the 5-classes DS2 dataset is 74%. However, the proposed approach using ResNet achieves a sensitivity of 85.5%. The level of confidence that is associated with the classifications that are made by the model is another important factor that should be taken into consideration. In general, models that are more accurate tend to demonstrate a higher level of confidence. To be more specific, the data presented in Tables 8 and 11 indicates that DS3 generates a higher level of confidence in comparison to DS2, particularly for our most advanced models. Because of this, the 7-class method provides advantages not only in terms of sensitivity but also in terms of the levels of confidence it provides. According to the results of our experiments, the ResNet model in DS3 is the one that performs the best when it comes to HER2 prediction. In general, it demonstrates a high level of sensitivity (91%), and it also demonstrates higher levels of confidence, ranging from 87% to 97%. In contrast, the DenseNet model is a little bit behind the curve, with confidence levels ranging from 84% to 92%.