# Ulcerative Severity Estimation Based on Advanced CNN-Transformer Hybrid Models
## 3 Results
### Key Findings
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**4-Score**} & \multicolumn{3}{c}{**Remission**} \\ \cline{2-7}
**Model** & **Accuracy** & **QWK** & **F1-Score** & **Accuracy** & \(kappa\) & **F1-Score** \\ \hline CoAtNet\_0 & \(0.7981\pm 0.0040\) & \(0.8745\pm 0.0044\) & \(0.7381\pm 0.0085\) & \(0.9610\pm 0.0026\) & \(0.8634\pm 0.0097\) & \(0.8869\pm 0.0082\) \\ CoAtNet\_1 & \(0.8025\pm 0.0040\) & \(0.8791\pm 0.0075\) & \(0.7410\pm 0.0075\) & \(0.9613\pm 0.0018\) & \(0.8654\pm 0.0072\) & \(0.8889\pm 0.0062\) \\ CoAtNet\_2 & \(0.8056\pm 0.0093\) & \(0.8824\pm 0.0084\) & \(0.7428\pm 0.0128\) & \(0.9630\pm 0.0042\) & \(0.8737\pm 0.0012\) & \(0.8962\pm 0.0093\) \\ CoAtNet\_3 & \(0.8054\pm 0.0072\) & \(0.8791\pm 0.0067\) & \(0.7426\pm 0.0075\) & \(0.9620\pm 0.0030\) & \(0.8701\pm 0.0084\) & \(0.8932\pm 0.0066\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification results for the complete 4-level Mayo scores and remission tasks across four CoAtNet model specifications. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**4-Score**} & \multicolumn{3}{c}{**Remission**} \\ \cline{2-7}
**Model** & **Accuracy** & **QWK** & **F1-Score** & **Accuracy** & \(kappa\) & **F1-Score** \\ \hline CoAtNet\_2 & \(0.8056\pm 0.0093\) & \(0.8824\pm 0.0084\) & \(0.7428\pm 0.0128\) & \(0.9630\pm 0.0042\) & \(0.8737\pm 0.0012\) & \(0.8962\pm 0.0093\) \\ Inception\_v3 * & \(0.7880\pm 0.0110\) & \(0.8678\pm 0.0060\) & \(0.7261\pm 0.0150\) & \(0.9590\pm 0.0030\) & \(0.8598\pm 0.0120\) & \(0.8847\pm 0.0100\) \\ ResNet18 * & \(0.7740\pm 0.0110\) & \(0.8568\pm 0.0100\) & \(0.7055\pm 0.0210\) & \(0.9566\pm 0.0050\) & \(0.8521\pm 0.0160\) & \(0.8785\pm 0.0130\) \\ MobileNet * & \(0.7759\pm 0.0100\) & \(0.8588\pm 0.0060\) & \(0.7254\pm 0.0100\) & \(0.9588\pm 0.0050\) & \(0.8592\pm 0.0120\) & \(0.8842\pm 0.0100\) \\ Overlock\_b & \(0.7908\pm 0.0042\) & \(0.8670\pm 0.0040\) & \(0.7225\pm 0.0101\) & \(0.9574\pm 0.0026\) & \(0.8514\pm 0.0088\) & \(0.8771\pm 0.0074\) \\ \hline \hline \end{tabular}
* Note: The results for Inception_v3, ResNet18, and MobileNet_V3_Large (referred to as MobileNet in this table) are cited directly from their respective original publications [23].  
\end{table} 
Table 2: Performance comparison between the CoAtNet-2 model and CNN models based on the complete 4-score Mayo score and remission classification. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**4-Score**} & \multicolumn{3}{c}{**Remission**} \\ \cline{2-7}
**Model** & **Accuracy** & **QWK** & **F1-Score** & **Accuracy** & \(kappa\) & **F1-Score** \\ \hline CoAtNet\_2 & \(0.8056\pm 0.0093\) & \(0.8824\pm 0.0084\) & \(0.7428\pm 0.0128\) & \(0.9630\pm 0.0042\) & \(0.8737\pm 0.0012\) & \(0.8962\pm 0.0093\) \\ DeiT & \(0.7929\pm 0.0069\) & \(0.8690\pm 0.0055\) & \(0.7309\pm 0.0121\) & \(0.9590\pm 0.0038\) & \(0.8573\pm 0.0139\) & \(0.8822\pm 0.0117\) \\ MaxViT & \(0.8034\pm 0.0060\) & \(0.8801\pm 0.0041\) & \(0.7455\pm 0.0105\) & \(0.9612\pm 0.0017\) & \(0.8668\pm 0.0067\) & \(0.8904\pm 0.0059\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison between the Coatnet_2 model and pure transformer models based on the complete 4-level Mayo score and remission classification. Moreover, we conducted ROC and AUC analyses to provide a more intuitive comparison of the classification capabilities of different models across each category from a new viewpoint. As shown in Figure 4, the performance of each model in the four-score plus remission classification task is illustrated using ROCs based on the one-vs-rest strategy for each class. The corresponding macro-averaged AUC values were also calculated. These observations imply that both the individual class AUCs and the macro-average AUCs were close to or exceeded 0.90, demonstrating the models' strong and consistent classification performance across all categories. In terms of the remission classification, the AUC results were 0.99, which revealed high classification performance.