# Transfer Learning-Based Classification of Maximly Sinus Using Generative Adversarial Networks
## 3 Methodology
### Data Labeling  
The data labeling process involved consultation with professionals in the field, including two experienced radiologists specializing in sinus-related pathologies. Out of the total 2146 collected images, 1320 images were selected to be labeled by two experienced radiologists using the Lund-Mackay rating method to quantify the severity of the images. The labeled images, taken in the coronal view, were categorized into three classes: 0 (Moderate Sinus Cases), 1 (Severe Sinus Cases), and 2 (Normal Cases), as shown in Figure 3. After removing images of low image quality or those not belonging to the predefined categories, the final dataset comprised \(n\) = 1320 labeled images. The Lund-Mackay scoring system is considered to be one of the most commonly used approaches to classify the severity of ethmoid sinus pathologies in the case of sinusitis [41]. The Lund-Mackay scoring system evaluates various anatomical regions of the nasal cavity and paranasal sinuses based on the extent of opacification observed on CT scans or imaging studies. For instance, a score of 0 indicates no opacification in either maxillary sinus, while a score of 1 signifies partial opacification in one or both maxillary sinuses, and a score of 2 denotes complete opacification in one or both maxillary sinuses. Furthermore, the decision to focus on training only the Moderate and Severe classes was made to address the issue of imbalanced classes, prioritizing the severity levels that are of greater clinical relevance. The strengths of this system include the systematic and comprehensive analysis of several sinus regions, leading to an objective measure of disease severity. On the other hand, there is a limitation in its use, in its subjective interpretation, for the scoring depends on the individual differences in assessing the opacification. Figure 3: CT scans’ illustration of severity levels. Figure 2: Temporal trends and monthly variability of sinusitis cases. For the consistency of the results, we conduct a reliability test using Cohen's Kappa coefficient. It measures the agreement among the consultants and experts in the labeling process. Within any data labeling process such as the Lund-Mackay scoring system, Cohen's Kappa serves as a standard metric for measuring the reliability and consistency of annotations [42]. Its advantage lies in providing a more robust measure than a simple percentage agreement, accounting for agreements occurring by chance. However, Cohen's Kappa can show sensitivity to differences in the category distribution, and the condition interpretation may be modified due to the rate of cases observed. The Cohen's Kappa (\(\kappa\)) statistic measures the level of agreement between two raters, with values ranging from 0 to 1. The interpretation of \(\kappa\) values suggests slight agreement if \(\kappa\) is between 0.01 and 0.20, fair agreement from 0.21 to 0.40, moderate agreement from 0.41 to 0.60, substantial agreement from 0.61 to 0.80, and almost perfect agreement from 0.81 to 1 [42]. In the calculation of [PERSON]'s Kappa coefficient for inter-rater reliability, the total observations were determined by summing all values in the contingency table as shown in Table 4, resulting in 1320. The total observed agreement, representing the sum of the diagonal values indicating agreement between the raters, amounted to 1255. Dividing the total observed agreement by the total observations yielded a proportion of 0.9515, denoted as \(\mathrm{P_{o}}\), reflecting the observed agreement rate. To calculate the expected agreement (\(\mathrm{P_{e}}\)) for [PERSON]'s Kappa coefficient, the proportion of agreements expected by chance for each class was computed. For Class 0, the expected agreement was determined as 189.39. Likewise, for Class 1 and Class 2, the expected agreements were calculated as 68.18 and 156.35, respectively. Summing these values provided the total expected agreement, resulting in 413.92. Dividing the total expected agreement by the total observations yielded a proportion of 0.3138 for \(\mathrm{P_{e}}\).