## 4 Experiments and Results
### Discussions  
DFU-Helper is a framework that utilizes Siamese Similarity Learning to assist medical practitioners in validating follow-up diagnostics for treatment purposes. While our model demonstrates a strong correlation with the evaluation conducted in the research articles from which we sourced the test images, we were unable to assess its performance on real longitudinal DFU images, as was done by [PERSON] et al. (2019) in their studies on retinopathy of prematurity and osteoarthritis. [PERSON] et al. (2019) had a database of longitudinal images consisting of 4861 images from 870 patients for retinopathy of prematurity, and 10,012 images from 3021 patients for osteoarthritis. In contrast to our approach of showing progress across five  
Figure 20: Longitudinal Similarity: Euclidean distance, part 2. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Time** & **None** & **Infection** & **Ischemia** & **Both** & **Healthy** \\ \hline
**Day 1** & **0.29** & 0.20 & 0.07 & 0.00 & **0.29** \\ \hline
**Day 2** & 0.19 & **0.33** & 0.1 & 0.00 & 0.18 \\ \hline
**Day 3** & 0.00 & **0.67** & 0.12 & 0.02 & 0.00 \\ \hline
**Day 4** & 0.03 & **0.63** & 0.14 & 0.00 & 0.03 \\ \hline
**Day 5** & 0.00 & **0.59** & 0.21 & 0.15 & 0.00 \\ \hline
**Day 6** & 0.00 & **0.64** & 0.16 & 0.22 & 0.00 \\ \hline
**Day 7** & 0.00 & **0.44** & 0.23 & 0.40 & 0.00 \\ \hline
**Day 8** & 0.01 & 0.09 & 0.16 & **0.53** & 0.00 \\ \hline
**Day 9** & 0.00 & 0.18 & 0.18 & **0.64** & 0.00 \\ \hline
**Day 10** & 0.00 & 0.22 & 0.22 & **0.41** & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results by applying Euclidean distance for Use-Case #3.  
class anchors, [PERSON] et al. [24] employed an overall severity score and compared individual images against the established normal standards for both eyes and knees, providing a corresponding score as output. As far as the architecture of our SNN is concerned, as opposed to all the work detailed in the related works sections, including those for severity evaluation over a time period [24; 26; 27], none uses an ensemble of CNN and ViT for the sub-networks. For CNN, we use EfficientNet, which is known to achieve state-of-the-art results while dealing with images, the novel and widely used ViT, and more specifically, the BEiTV2 [59]. When tested on a classification task, this architecture has a Macro F1-Score of 0.951 and an accuracy of 0.9395. The majority of work are concentrated on using CNN backbones namely VVG16, ResNet18, and InceptionV3. In the study conducted by [PERSON] et al. [32], they developed a detection method for COVID-19 by creating an ensemble of six pre-trained CNNs and the Vision Image Transformer (ViT), specifically the Swin Transformer, along with a Triplet Siamese Neural Network framework. Although we have concerns about the computational resources required, the concept of integrating cutting-edge models to achieve optimal results in the medical field is acceptable. As part of our future work, we propose evaluating the top-performing ensemble CNN and the top-performing ensemble ViT separately and subsequently combining the best ensembles to further improve detection accuracy. In the selection of anchors for image comparison over time in DFU-Helper, we took a different approach compared to [PERSON] et al. [26] and [PERSON] et al. [27]. Instead of using 5 or 16 images per class, we utilized the maximum number of available images per class in our dataset to generate the class anchors. For training DFU-Helper and SNN, we introduced a novel loss function called Large Margin Cotangent (LMCoT), proposed by [PERSON] et al. [44], deviating from the commonly used contrastive loss or triplet loss functions in the relevant works we considered for severity estimation, disease detection, or classification. Interestingly, [PERSON] et al. [27] explored four different loss functions, including contrastive loss, mean square error (MSE) loss, Huber loss, and a combination of contrastive and MSE loss, which holds its own merit. In contrast to non-deep learning methods, [PERSON] et al.