## 2 Background and Related Work
### Transfer Learning  
In the context of deep neural networks, it is uncommon to have a sufficiently large dataset to train an entire convolutional neural network (CNN) from scratch. Deep neural networks, with their numerous parameters, face challenges when trained on small datasets, impacting their ability to generalize and often resulting in overfitting [18]. Transfer learning (TL) originates from cognitive research, wherein the concept revolves around the notion that knowledge can be transferred between related tasks to enhance performance in a novel task. It is widely acknowledged that humans excel at solving similar tasks by leveraging their previous knowledge [24]. Decision support systems leveraging deep learning (DL), machine learning (ML), and neural networks (NNs) exhibit a high level of efficiency but are hindered in the medical field by limited comprehensive datasets. Despite advancements in machine learning, the development of DSSs for CD diagnosis is relatively unexplored, likely due to the complexity of the issue and the scarcity of public databases. [PERSON] et al. [25] provide an overview of contemporary developments in computer-aided diagnoses of celiac disease utilizing upper endoscopies. They suggest frameworks for achieving fully automated patient-specific diagnoses and for incorporating expert knowledge into the automated decision-making process. In [26], the authors presented a feature descriptor tailored for the classification of video capsule endoscopy images. Furthermore, they introduced a system that utilizes deep convolutional neural networks (CNNs) to characterize small intestine motility by detecting individual motility events. In recent years, deep learning methods, including prominent convolutional neural networks like AlexNet, GoogLeNet, and VGG16 net, have been increasingly utilized for the classification of endoscopic images. [PERSON] et al. [27] propose a deep-learning-based approach aimed at recalibrating modules to distinguish images featuring regions significant for celiac disease from images of healthy ones. Their method, employing support vector machine and k-nearest neighbor modules for a linear discriminant analysis, achieved impressive results of a 95.94% accuracy, 97.20% sensitivity, and 95.63% specificity using a 10-time 10-fold cross-validation strategy. [PERSON] et al. [28] devised a methodology utilizing a fuzzy cognitive map (FCM) alongside a possibilistic fuzzy C-means clustering algorithm (PFCM) to categorize celiac disease (CD). Their research objective aimed at developing an expert system capable of classifying CD patients into three grades: A, B1, and B2, representing the latest available grading method. Three experts identified seven key defining features of CD, which were treated as FCM concepts. The authors achieved accuracy rates of 88%, 90%, and 91% forthe respective analysis classes. The experimental results revealed an impressive mean classification accuracy of 96% for six distinct intestinal motility events. To our understanding, there has been a lack of development in clinical support systems tailored specifically for celiac disease screening, particularly those intended to operate based on non-invasive diagnostic tests with the aim of reducing reliance on biopsies. This gap in research likely stems from both the scarcity of screening data and the intricate nature of the diagnostic challenge. This study aims to investigate whether transfer learning algorithms can achieve a sufficient accuracy in classifying facial images to aid primary care physicians in making referrals to specialists for the diagnosis of CD. VGG16 architecture was employed in transfer learning for classifying facial images of people with celiac disease to aid the diagnosis of CD. VGG16 is a pre-trained model known for its simplicity and efficiency in learning hierarchical features from images through its deep convolutional layers [29].