## 4 Discussion
This methodology is crucial because omitting affine augmentations from certain sets while only augmenting the base data could skew results. Affine approaches have consistently demonstrated effectiveness in enhancing model performance and mitigating overfitting. Therefore, to avoid giving an unfair advantage to these approaches, our study incorporates them uniformly across all datasets. This strategy ensures that our primary comparison point--the baseline set without base data modifications--remains a reliable standard for evaluating the effectiveness of base data augmentation within this naturalistic paradigm. In the \"No ROI\" image, the upper portion of the patella is visible, along with the upper femur and the medial and lateral femoral condyles. The lower portions of the tibia and fibula are also evident. However, the entire medial and lateral tibial plateaus are missing. The Grad-CAM visualizations for these \"No ROI\" images show activations concentrated on peripheral regions of the knee joint, such as the edges of the femur and tibia that do not articulate directly. Consequently, the omission of the tibial plateaus means that the critical weight-bearing surfaces of the knee joint, essential for assessing osteoarthritis, are absent from the image. The joint space, crucial for evaluating the degree of cartilage loss and joint space narrowing, cannot be visualized either. The absence of the articulating surfaces largely prevents the assessment of osteophyte presence, subchondral bone sclerosis, and cyst formation, all key features used to grade the severity of osteoarthritis according to the Kellgren and Lawrence grading system. Further insights from our Grad-CAM visualization analysis indicate potential confounding regions that might affect our models' decision-making processes. Notably, in the absence of a designated region of interest (ROI) for KL0, the models tend to focus on the texture and contours of the patella. Interestingly, this pattern shifts with the baseline KL0 models, where the joint and its eminence are distinctly highlighted. However, the spread of activation that extends broadly across and above the knee joint suggests the model might be considering features beyond the knee joint for classification. In the No ROI Split KL4, the model appeared to be using general wear-and-tear texture indications for their classifications, which may not be directly related to disease progression but rather to the participant's age. Finally, in the KL1 category, the models oscillate between specific and non-specific textures, further underscoring potential confounding regions. Regarding Grad-CAM limitations, it is important to highlight that Grad-CAM has several limitations that can lead to misinterpretations. Firstly, it often highlights correlations rather than causations, meaning the regions it emphasizes may not be causally linked to the prediction. It is also limited to convolutional layers, providing no insight into how other types of layers contribute to the model's decision. Grad-CAM can fail to localize multiple instances of the same class, and for single class examples, it may fail to localize the entire region of the class [60]. Users might over-rely on these visual explanations for model validation, potentially overlooking other issues like biases in training data. Furthermore, Grad-CAM may also fall short in capturing complex, abstract reasoning that isn't spatially localized. Its effectiveness is tied to model architectures, and different architectures may yield varying results, limiting generalizability. Additionally, if a model is overfitted, Grad-CAM might highlight non-generalizable, idiosyncratic features. Lastly, it often misses the global context of the image, focusing only on local features, which may not always convey the complete picture necessary for accurate interpretation. These limitations underscore the importance of using Grad-CAM cautiously and in conjunction with other validation methods to ensure a comprehensive understanding of model behavior.