# Transfer Learning-Based Classification of Maximly Sinus Using Generative Adversarial Networks
## Perceptual Similarity
#### 3.4.6 Transfer Learning Models  
In this study, we utilize the transfer learning approach for classification. ResNet-50 and ResNeXt-50 are used for the classification of sinusitis severity from CT images. Leveraging the learned features from large-scale datasets, these models offer a powerful framework for extracting relevant features and achieving robust performance in medical image analysis tasks.  
Additionally, we incorporated several augmentations during the training process to further diversify our dataset. These augmentations, listed in Table 8, include rotation with a range of 20 degrees, horizontal and vertical shifts with a range of 0.15, horizontal flipping, nearest neighbor filling mode, zooming with a range of 0.1, and shearing with a range of 0.15.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class**} & **Total True** & **Images Similar** & **Remaining True** & **10\% Random Selection** \\  & **Images** & **to Generated** & **Images** & **for Validation** \\ \hline Moderate & 292 & 152 & 140 & 14 \\ \hline Severe & 264 & 169 & 95 & 10 \\ \hline Normal & - & - & 764 & 76 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Generation of validation dataset for model evaluation.  
Figure 10: Data splitting process.  
ResNet-50  
ResNet-50, or Residual Network with 50 layers, is a deep convolutional neural network architecture developed by [PERSON] et al. [45]. It is a member of the ResNet family, which is recognized for its novel method of leveraging residual connections to solve the vanishing gradient problem during training. ResNet-50's core architecture includes 50 layers, which include convolutional, pooling, and fully connected layers [45]. ResNet's distinguishing characteristic is the use of skip connections or shortcuts to bypass one or more levels, allowing the network to learn residual mappings. These residual connections make it easier to train very deep networks by allowing for the direct passage of gradients during backpropagation, addressing the degradation problem that standard deep networks encounter.  
ResNet-50 was chosen because of its shown performance in a variety of computer vision applications, such as imagine classification, object identification, and image segmentation [46; 47]. Its deep design allows it to learn nuanced characteristics from images, making it appropriate for challenging tasks like sinusitis severity categorization using CT scans.  
ResNeXt-50  
ResNeXt-50 is a modified version of the ResNet architecture, which was developed by [PERSON] et al. [48]. It draws on the ResNet design philosophy, but adds an additional concept called cardinality to increase the model's representational capability [48]. ResNeXt-50 has a similar design to ResNet-50, consisting of several residual blocks linked together via skip connections. However, ResNeXt-50 adds a new dimension to the architecture: cardinality, which reflects the number of distinct pathways within each residual block. ResNeXt-50 improves model performance by increasing cardinality, which increases the model's ability to collect varied characteristics and patterns from input data. ResNeXt-50 outperforms typical ResNet designs in terms of generalization and scalability because of the additional parallelism afforded by many pathways inside each block [48].  
Just like ResNet-50, ResNeXt-50 is also chosen for its high performance and scalability in a variety of computer vision workloads. Its capacity to capture a variety of characteristics makes it ideal for tasks that need complicated and heterogeneous data, such as medical image analysis [49; 50]. ResNeXt-50, like ResNet, has pre-trained versions that allow for quick transfer learning and adaption to specific tasks with little labeled input.