## 5 Discussion  
This study highlights the potential of transfer learning, particularly the VGG16 model, in the diagnosis of celiac disease through the classification of facial photos. The model achieved a 73% accuracy with sensitivity, recall, and F1 score values of 0.54, 0.56, and 0.52, respectively. While these results are promising, they also highlight challenges and areas for improvement.  
The model demonstrated a moderate level of accuracy, demonstrating its ability to identify subtle facial features associated with celiac disease. However, the balanced but moderate levels of sensitivity and recall suggest a need for improvement.  
The dataset was limited in size due to variations in sex ratios and dietary compliance levels, which provided variability. This small dataset likely contributed to performance limitations and highlights the need for larger and more diverse datasets. Classifying facial  
Figure 8: Training loss, validation loss, training accuracy, and validation accuracy versus number of epochs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1 Score** & **Support** \\ \hline Celiac & 0.57 & 0.56 & 0.52 & 50 \\ \hline Nonceliac & 0.54 & 0.70 & 0.61 & 50 \\ \hline Accuracy & & & 0.55 & 100 \\ \hline Macro Avg & 0.55 & 0.55 & 0.54 & 100 \\ \hline Weighted Avg & 0.55 & 0.55 & 0.54 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance of the classification model.  
images, which can be challenging even for experts, posed a significant challenge. Variability in facial features due to irrelevant factors, such as genetics and age, further complicated the model's task.  
Transfer learning with VGG16 accelerated the training process and achieved a reasonable performance with a small dataset. Resizing images from 1600 \(\times\) 2000 to 224 \(\times\) 224 pixels, as required by models such as VGG16, can introduce artifacts and obscure fine details. While these models can still extract robust features, the loss of fine details in patients' facial photographs can negatively impact model performance. Future work can investigate preserving critical facial details during preprocessing to improve accuracy. Furthermore, since we intend to create a web-based system to which patients can upload images for prediction purposes in our future work, these images may vary in size and may even be low-resolution. Furthermore, we compared a fixed training-test split (using the holdout method) and K-fold cross-validation. The K-fold cross-validation resulted in a lower accuracy due to the difficulty distinguishing between classes, suggesting the need for more data or different modeling approaches. Consequently, we chose the holdout method for its higher accuracy and continued the process.  
Future research using a larger dataset is recommended. Increasing the dataset size and ensuring balanced representation can improve the performance of the model by providing a more diverse set of training examples. However, further investigation into specific facial features used by the model may provide insights into the physical symptoms of celiac disease. Facial photo classification can be integrated in the future with other diagnostic tools using a multimodal approach; for example, combining it with serological tests or genetic markers may improve its diagnostic accuracy.  
This study demonstrates the feasibility of transfer learning with VGG16 for the diagnosis of celiac disease through facial photo classification, achieving a 73% accuracy. While the model is promising, expanding and diversifying the dataset and improving the model can significantly improve its performance. This research forms the basis for developing a reliable diagnostic tool for celiac disease by utilizing transfer learning in medical image analysis.