# Transfer Learning-Based Classification of Maximly Sinus Using Generative Adversarial Networks
## 2 Related Studies
### Convolutional Neural Network (CNN)  
CNNs, a subset of deep learning techniques, have garnered considerable attention for their remarkable efficacy in image analysis and classification tasks. Their hierarchical architecture, characterized by convolutional layers for feature extraction and pooling layers for spatial down-sampling, enables the automatic learning of intricate patterns and representations within medical images. As evidenced by various studies in the literature, CNNs have demonstrated exceptional performance in tasks ranging from detecting and diagnosing diverse medical conditions to segmenting anatomical structures with high precision. Authors have employed CNNs in the context of sinus-related pathologies and imaging modalities. Table 1 presents a comparison of these studies.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref** & **Year** & **Problem** & **Dataset** & **Method** & **Results** & **Limitation** \\ \hline \multirow{4}{*}{[13]} & \multirow{2}{*}{2023} & Deep learning model & \multirow{4}{*}{CBCT images} & \multirow{4}{*}{CNN} & \multirow{4}{*}{AUROC: 0.953} & Potential difficulties and solutions for large-scale intelligent disease & \\  & & & sinus abnormalities & & & & applications \\ \hline \multirow{4}{*}{[14]} & \multirow{2}{*}{2022} & Automated CNN-based & \multirow{4}{*}{CBCT images} & \multirow{4}{*}{CNN} & Dice Similarity Coefficient (DSC)—98.4\% & Lack of data \\  & & & & & & \\ \hline \multirow{4}{*}{[15]} & \multirow{2}{*}{2022} & Multi-view CNN for & Radiographs (Waters’ & \multirow{4}{*}{CNN} & \multirow{4}{*}{AUC: 0.750} & Improving predictive \\  & & estimating sinusitis & view and Caldwell’s & & & & ability with a shallow \\  & & severity & view). & & & & model \\ \hline \multirow{4}{*}{[16]} & \multirow{2}{*}{2022} & Developing a CNN & \multirow{4}{*}{PRs and CBCT images} & \multirow{4}{*}{CNN} & \multirow{4}{*}{Accuracy: 99.7\%} & Potential insufficiency of \\  & & model for diagnosing & & & & & imaging methods alone \\  & & maxillary sinusitis & & & & & for maxillary sinusitis \\ \hline \multirow{4}{*}{[17]} & \multirow{2}{*}{2023} & Application of digital & \multirow{4}{*}{49 conditionally healthy } & \multirow{4}{*}{CNN and } & \multirow{4}{*}{0.88 and } \\  & & diaphanoscopy for & & volunteers and & & & \\ \cline{1-1}  & & detecting maxillary & \multirow{4}{*}{42 patients} & \multirow{4}{*}{LDA} & \multirow{4}{*}{0.98} & \multirow{4}{*}{0.98} \\ \cline{1-1}  & & sinus pathologies & & & & & \\ \hline \multirow{4}{*}{[18]} & \multirow{2}{*}{2019} & Diagnosing maxillary & \multirow{4}{*}{[PERSON] view } & [PERSON] view & \multirow{4}{*}{CNN} & \multirow{4}{*}{AUC: 93\%} \\ \cline{1-1}  & & sinusitis on Waters’ & radiographs & radiographs & & & concurrent CT in training \\ \hline \multirow{4}{*}{[19]} & \multirow{2}{*}{2022} & Automated analysis of & CT scans from 140 & \multirow{4}{*}{CNN} & \multirow{4}{*}{0.852\%} & Transfer learning methods \\  & & paranasal sinuses & & & University & & are ideal for small dataset \\ \hline \multirow{4}{*}{[20]} & \multirow{4}{*}{2021} & \multirow{4}{*}{2022} & CT scans of 2349 & \multirow{4}{*}{CNN} & AUC of 0.71, \\  & & & consecutive patients & & & 0.78, and 0.88 \\ \cline{1-1}  & & [PERSON] and [PERSON] & & & & \\ \cline{1-1}  & & views & & & & \\ \cline{1-1} \cline{2-1} \cline{