## 3 Results
### Summary of the Models' Performance  
1. VGG-19-based model  
A model was used to accurately differentiate between lung images with COVID-19-induced pathological tissue changes and those images without such changes. The number of true-negative and true-positive cases was much higher than that of false-positive and false-negative cases, indicating that the model did not randomly assign objects to either of the two available classes. The model's performance was assessed (Figure 7) using a confusion matrix and ROC curve, which showed that 75% of the processed images were correctly categorized into one of the two classes. The final accuracy achieved by the model was considered appropriate for research settings. Therefore, the model accurately distinguished between pathological changes induced by COVID-19 and healthy lungs. 2. InceptionResNetV2-based model  
This model was trained to categorize objects into three classes, as shown in the confusion matrix. The ROC curve, however, only assessed the performance in differentiating COVID-19 cases from two other diagnostic possibilities. These results are shown in Figure 8.  
Figure 8: InceptionResNetV2-based model Testing results and performance evaluation: (a) ROC curve; (b) confusion matrix. 3 CAP—community-acquired pneumonia-infected patient, 2 COVID—COVID-19-infected patient, 1 Non-COVID—healthy patient. Figure 7: VGG-19-based model Testing results and performance evaluation: (a) ROC curve; (b) confusion matrix. 0—healthy patient, 1—COVID-19-infected patient. The confusion matrix indicated that the model effectively sorted images into categories without randomly assigning them. This was confirmed by the ROC curve, which showed a high number of true-positive and true-negative cases for all three conditions. The model performed best in detecting community-acquired pneumonia from lung CT slices, while it struggled the most with categorizing COVID-19-infected patients. Nonetheless, the confusion matrix and ROC curve demonstrated that the model correctly identified 84% of COVID-19 cases. Therefore, the model's final accuracy is suitable for research use. 3. Custom CNN model  
The confusion matrix and ROC curve demonstrated that the model accurately performed binary classification without randomly assigning images to either class. The ROC curve closely aligned with the left corner of the plot, indicating a high number of true-positive and true-negative cases (see Figure 9). The model achieved a diagnostic accuracy of 98.01%, as shown by both the confusion matrix and ROC curve. This result was considered satisfactory and demonstrates that the model can be applied in clinical research settings. Table 5 shows the model testing results. Organizing the results into a table facilitated a comprehensive and in-depth analysis of the statistical measures. This approach enabled a more straightforward comparison of the outcomes of the three models evaluated in this study. When reviewed, it was found that the custom CNN model achieved the highest diagnostic quality. Most of its cells corresponding to specific statistical measures were shaded in green, indicating superior performance compared to the other two models. It needs to be highlighted that the custom CNN model also demonstrated the highest accuracy, precision, specificity, and sensitivity, which are considered the most critical statistical measures. The results for the two other models investigated in this study can be considered satisfactory, but the custom CNN model clearly outperformed them. The VGG-19-based  
Figure 9: Custom CNN model Testing results and performance evaluation: (a) ROC curve; (b) confusion matrix. 0—healthy patient, 1—COVID-19-infected patient. model demonstrated the lowest diagnostic quality, while the InceptionResNetV2-based model produced better outcomes, with accuracy, precision, specificity, and sensitivity scores above 50% in both cases.