# Ulcerative Severity Estimation Based on Advanced CNN-Transformer Hybrid Models
## 3 Results  
### Key Findings  
In this study, the quadratic weighted kappa coefficient (QWK), accuracy, and \(F1\) score were employed as the primary metrics for model performance evaluation. Model performance was evaluated under two assessment schemes: a full four-grade Mayo score classification and a binary remission classification. In the binary classification setting, remission is defined as a Mayo score of 0 or 1, while active disease corresponds to a score of 2 or 3 [22]. Notably, the model was exclusively trained on the full Mayo score classification task, and its predictions were subsequently mapped to the remission categories for binary evaluation. CoAtNet provides four structural variants, defined by the number of blocks at each stage, assuming an input resolution of \(224\times 224\). In this study, we utilized the CoAtNet_2 architecture, which consisted of block configurations of (2, 2, 6, 14, 2) and corresponding hidden dimensions of (192, 192, 256, 512, 1014) across Stages S0 to S4, respectively. A kernel size of 3 was uniformly employed for all convolutional (Conv) and mobile inverted bottleneck convolutional (MBConv) blocks. The detailed parameter settings for other CoAtNet variants are available in the original publication [6]. Under identical training configurations, the complete four-level Mayo score classification results and the remission classification outcomes for the four models with different specifications are summarized in Table 1. The results indicate that, for the LIMUC dataset, the CoAtNet-2 model achieved the best performance. Meanwhile, the performance of all models significantly exceeded the current best benchmark on LIMUC. To further highlight the inherent advantages of the model architecture, we select the CoAtNet_2 model as a  
Figure 2: Test results of various models across different \(\alpha\) parameter settings. representative and compare it with CNN and Transformer models of varying structures. The results are presented in Tables 2 and 3, respectively. It can be observed from the results that although models such as MaxVit and Overlock incorporate a balance between local and global attention, their performance still shows a noticeable gap compared to the best CoAtNet model. To further compare the classification results of different models (CoAtNet_2, Deit, MaxVit, and Overlock) for each category, we provide the final confusion matrix of the model for the classification of different grades of ulcerative colitis. This matrix, representing the aggregate results from 10-fold cross-validation, visually illustrates the model's classification performance, as shown in Figure 3. It is worth noting that the confusion matrices for Inception-v3, MobileNet_large_v3, and ResNet18 are omitted, as they were presented in the research paper on the LIMUC dataset [23]. Based on the confusion matrix results, it is evident that all models achieved the highest classification performance on Mayo 0, followed by Mayo 3 or Mayo 1, while Mayo 2 was associated with the worst scores. This trend may be attributed to the imbalanced data distribution. Comparative analysis also shows that our models exhibited better classification ability on class Mayo 3 compared to traditional CNN models, and the CoaTNet model improved classification performance across all categories compared to all the other models, rather than being optimized for a specific class.