## 4 Discussion  
Our analysis has highlighted the marked effectiveness of certain positive data augmentations in improving model performance. Specifically, the 'Baseline Rotated' model showed the highest accuracy but second-best recall. Incorporating rotation into our baseline model could have increased its robustness to orientation changes in the images, contributing to its superior accuracy. Furthermore, the confusion matrix analysis demonstrated excellent recall for the KL0 and KL4 grades, a result that may be associated with the distinct radiographic features of these classes. Conversely, the 'Horizontal Split' model, which divided the image into two parts along the horizontal axis, performed the worst across all considered metrics. This could be because this approach might eliminate or distort crucial radiographic features, thereby reducing the model's ability to classify the images accurately. Notably, the results contradicted our initial expectation that the ROI models would outperform the baseline models, given the assumption that focusing on specific regions containing more relevant information would increase performance. However, the results indicated that models utilizing the entire image data might have a slight edge in performance over those focusing on a particular ROI, suggesting that potentially important information outside the ROI might be missed, or that confounds are integrated, inflating the performance. Furthermore, when evaluating performance, the recall metric offered an additional layer of insight. For instance, despite a slight drop in accuracy, the 'Baseline' model showed a higher recall than the 'Baseline Rotated' model, underscoring its capability to comprehensively identify relevant cases. This can be a critical consideration in fields like medical diagnostics, where certain classes might have greater consequences associated with misclassification. This observation revealed that when dealing with significantly skewed class distributions, utilizing the baseline modality may lead to more evenly distributed performance across the relevant classes. While data augmentation techniques have been widely adopted in deep learning, studies specifically investigating their effects in the medical imaging domain remain sparse. Often, the choice of augmentation techniques relies heavily on informal recommendations or generic best practices that aren't always tailored to the unique challenges and characteristics of medical images [37]. This lack of systematic exploration can lead to suboptimal model performance or even introduce biases. In this context, the studies by [PERSON] [37] and [PERSON] et al. [38] stand out as notable exceptions that delve into the intricacies of various additive augmentation methods and their impact on model performance in medical imaging tasks. The study by [PERSON] [37], spanning different medical imaging domains such as lung CT, mammography, and brain MR images, observed distinct patterns in the effectiveness of additive augmentation. For lung CT images, translating and shearing produced the highest accuracy of 0.857, whereas mere translation yielded the lowest at 0.610. In mammography images, the combination of translation, shearing, and clockwise rotation was most effective, achieving an accuracy of 0.833, while adding'salt-and-pepper' noise and shearing underperformed, achieving only 0.667. For brain MR images, the same combination of translation, shearing, and clockwise rotation outperformed other methods with an accuracy of 0.882, while adding'salt-and-pepper' noise and shearing showed the lowest accuracy at 0.624. Another investigation by [PERSON] et al. [38] explored different mammography additive augmentation techniques, producing varied results. Notably, the Shear augmentation achieved the highest training accuracy of 0.891 and a validation accuracy of 0.879. Conversely, Noise augmentation was the least effective, with training and validation accuracies of 0.625 and 0.660, respectively.