# Transfer Learning-Based Classification of Maximly Sinus Using Generative Adversarial Networks
## 5 Conclusions  
This work explores the effectiveness of applying GANs to expand datasets and enhance the quality of synthetic images for the training of deep learning models for the detection of sinus pathologies. Employing the lightweight GAN architecture, synthetic images were produced to overcome the challenge of limited training data, specifically for the Moderate and Severe categories. The incorporation of GANs not only increased the diversity and realism of the dataset but also improved the robustness and generalization ability of the classification models. The synthetic images were produced using thorough evaluation and selection processes to make them look as close to the real images as possible. This ensured a close resemblance, which then boosted the performance of the developed deep learning model. ResNeXt-50 was the top-performing model, outperforming ResNet-50 in terms of accuracy and precision in the diagnosis of sinus pathologies. Lastly, this study emphasizes the importance of synthetic data generation techniques for the performance gain in medical image analysis tasks and the use of GANs for diagnostic capabilities' enhancement in healthcare applications. Despite the fact that this study has made significant contributions, it is crucial to mention some limitations. One of the limitations is the small dataset size used in this study, which may limit the applicability of the developed deep learning models. Although the attempt to develop a diverse dataset via GAN-based augmentation was made, the size of the original dataset was still small, which may restrict the model's capability in grasping the full range of variability in sinus diseases. Additionally, the study focused more on Moderate and Severe classes, while the Normal class was overlooked which may impact the model's performance in detecting the less severe cases. Moreover, while GANs offer a promising approach for synthetic data generation, their effectiveness may vary depending on factors such as model architecture and hyperparameters, introducing variability in the quality of generated images. Future research can investigate the transferability and scalability of the developed models to different medical imaging modalities or clinical settings. Applying GAN data augmentation methods beyond sinus pathologies to other medical domains can widen the horizon of their application and, consequently, their usefulness in health care. Additionally, understanding the interpretability and explainability of deep learning models when trained on synthetic data is critical in gaining the trust and use of this technology in clinical practice. Incorporating domain knowledge as well as expert input into models  
Figure 16: ROC curves of each fold for the ResNeXt-50 model—validation data. and an iterative review in real-world clinical studies are also necessary for realizing the full potential of GANs and deep learning in medical image analysis and diagnosis. Also, future research may also involve leveraging larger datasets to further enhance the robustness and generalization capabilities of the developed deep learning models for sinus-related medical imaging. Conceptualization, M.A. and A.G.F. ; methodology, M.A. and A.G.F. ; software, M.A. ; validation, M.A. and A.G.F. ; investigation, M.A. and A.G.F. ; resources, M.A. ; data curation, M.A. and A.G.F. ; writing--original draft preparation, M.A. and A.G.F. ; writing--review and editing, M.A. ; visualization, M.A. ; supervision, A.G.F. and M.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Hail Health Cluster, Hail, Saudi Arabia, with number H-08-L-074-2023-72. Patient consent was waived by the IRBs because of the retrospective nature of this investigation and the use of anonymized patient data.