# Transfer Learning-Based Classification of Maximly Sinus Using Generative Adversarial Networks
## 2 Related Studies
### Generative Adversarial Networks in Medical Imagiting  
In the field of medical imaging, generative adversarial networks (GANs) have become very effective tools, providing creative ways to produce realistic and high-quality medical images. GANs make it easier to synthesize visuals in the context of medical imaging that closely imitate real patient data. This feature is especially helpful in situations where gathering a wide variety of datasets is challenging. For an array of purposes, including increasing training datasets, modeling uncommon clinical states, and improving the efficacy of diagnostic models, GANs have been used to create synthetic medical pictures. [PERSON] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **Year** & **Problem** & **Dataset** & **Method** & **Results** & **Limitation** \\ \hline \multirow{3}{*}{[29]} & \multirow{3}{*}{2020} & Identifying concha & CT scans from a & \multirow{3}{*}{Inception-V3} & \multirow{3}{*}{Accuracy: 81\%} & Potential false negatives \\  & & bullosa on coronal & & rhinology & & due to concha bullosa \\  & & sinus & hospital in & & & presence at different slices \\ \hline \multirow{3}{*}{[30]} & \multirow{3}{*}{2019} & Individually & \multirow{3}{*}{CT data of 57} & \multirow{3}{*}{Darknet-19 and } & IoU has been & \multirow{3}{*}{Relatively small dataset,} \\  & & & nasal cavity in CT & patients & YOLO & & and need for further \\  & & scans & & & in 0.1 steps & validation \\ \hline \multirow{3}{*}{[31]} & \multirow{3}{*}{2024} & Developing CDSS for & \multirow{3}{*}{OASIS-3 MRI} & \multirow{3}{*}{Vertex AI} & \multirow{3}{*}{Precision: 0.928} & \multirow{3}{*}{
\begin{tabular}{c} Reliance on single coronal \\ 2D MRI slice, potential \\ variations in real-world \\ MRI scans \\ \end{tabular} } \\  & & & & & MRI scans \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **Year** & **Problem** & **Dataset** & **Method** & **Results** & **Limitation** \\ \hline \multirow{3}{*}{[32]} & \multirow{3}{*}{2023} & Predicting Maximly & Data from 150 & \multirow{3}{*}{ML algorithm} & R-squared & \multirow{3}{*}{Small sample size} \\  & & Sinus Volume using & patients with normal & & & \\  & an ML algorithm & maxillary sinuses & & & 0.98\% & \\ \hline \multirow{3}{*}{[33]} & \multirow{3}{*}{2021} & End-to-end process & \multirow{3}{*}{Waters’ view X-ray} & Independent & \multirow{3}{*}{(AUC): 88.93\%} & Lack of paranasal \\  & & for maxillary & images & & task learning & (0.89), 91.67\% & computed tomography \\ \cline{1-1} \cline{4-4} \cline{6-6}  & & & & & & verification, absence of \\ \cline{1-1} \cline{4-4} \cline{6-6}  & & & & & normal maxillary sinus & \\ \cline{1-1} \cline{4-4} \cline{6-6}  & & & & information in training & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of conventional techniques. et al. [34] proposed a data-driven approach using a generative adversarial network (GAN) to address the challenge of estimating computed tomography (CT) images from Magnetic Resonance Imaging (MRI) data without radiation exposure. The proposed method involves training a fully convolutional network (FCN) with an adversarial training strategy to better model the nonlinear mapping from MRI to CT. The use of an image-gradient-difference-based loss function aims to reduce blurriness in the generated CT images. Also, [PERSON] et al. [35] discussed the challenges of limited and privacy-constrained medical imaging data and proposed a two-stage pipeline for generating synthetic medical images using generative adversarial networks (GANs). The focus is on overcoming data scarcity and privacy concerns by leveraging GANs to create synthetic medical images, particularly demonstrated in retinal fundi images. The pipeline involves a hierarchical generation process, separating the task into geometry and photorealism. Most importantly, GANs also have an application in diagnosis of sinus-related conditions. For example, [PERSON] et al.