## 2 Related Works
The authors investigated the performance of their model using four different loss functions: contrastive loss, mean square error (MSE) loss, Huber loss, and a combination of contrastive and MSE loss. [PERSON] et al. [29] leverage the characteristics of SNNs needing small samples of data for training. They introduced a Siamese neural model that uses a triplet loss function that enables the gastroenterologist to inject anchor images that can correctly identify the ulcerative colitis severity classes. To monitor the severity over time, a triplet loss function is applied. Gastroenterologists inject anchor images that can correctly identify the ulcerative colitis severity by using the classes and using the Mayo Clinic Ulcerative Colitis Endoscopic Scoring scale. During our research, we also explored non-deep-learning-based methods for assessing image similarity in the context of medical images. [PERSON] et al. [30] proposed a method that relies on feature extraction and analysis. They applied their method to rat brain histological images and compared the similarity estimates with expert evaluations to demonstrate its effectiveness. Their approach involved various computer vision techniques, such as color model conversion, image normalization, anti-noise filtering, contour detection, conversion, and feature analysis. The feature search process utilized an anchor image. [PERSON] et al. [31] conducted a study on image similarity using 54 video files of endocapsules labeled by gastroenterologists. They employed techniques based on color histogram and Local Binary Patterns (LBP) Histogram and calculated the difference be tween image pairs to determine similarity. A value close to 0 indicated higher similarity. These studies demonstrate alternative approaches to assessing image similarity in medical imaging, utilizing feature-based methods and computer vision techniques instead of deep learning-based approaches. Now, we move on to explore the use of SNNs in the medical domain applied to images. These studies presented above show methodologies for evaluating image similarity in medical imaging, employing feature-based methods and computer vision techniques as alternatives to deep learning-based approaches. Now, we move our focus to investigating the application of Siamese Neural Networks (SNNs) in the medical domain, specifically in the medical field, using image analysis. [PERSON] et al. [32] introduced a Siamese few-shot learning model for early detection of COVID-19, aiming to mitigate the long-term effects of this dangerous disease. Their proposed architecture combined few-shot learning with an ensemble of pre-trained Convolutional Neural Networks, enabling the extraction of feature vectors from CT scan images for similarity learning. They implemented a Triplet Siamese Network for classification, utilizing six transfer-learning-based models (ResNetV2, DenseNet, SwinTransformer, MobileNetV2, EfficientNetB0, ResNeXt-101) as the backbone of the network to create an ensemble model. This ensemble model generated embeddings for each image in the input triplet, enhancing the accuracy of the classification. [PERSON] et al. [33] implemented a Siamese Neural Network with a VGG-16 backbone and CNN for multiclass classification of Alzheimer's Disease. They utilized the network to classify different stages of the disease. [PERSON] et al. [34] proposed a novel Siamese Convolutional Neural Network (CNN) architecture using InceptionV3 [35] as the backbone. Their work focused on binary classification, and they employed the Adam optimizer for training. [PERSON] et al. [36] conducted research on the classification of heartbeats using 12-Lead ECG datasets. They developed a Siamese Neural Network based on CNN for this task. Table 1 provides a summary of other related works in the medical field that have explored the use of Siamese Networks. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  
Table 1 displays additional research where a Siamese Neural Networks is employed.