# Deep Learning for COVID-19 Diagnosis from CT Images
## 4 Discussion
### On the Three-Class Classification on COVIDx CT-2A
#### 4.1.1 On the Patient-Oriented Classification on COVIDx CT-2A  
The peculiarity of misclassification of CT images of patients belonging to the _COVID-19_ class affects every network. In some cases in a more pronounced form and in others \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Net** & **Pre** & **Rec** & **Spe** & **Acc** & **F1** \\ \hline VGG19 & 52.23\% & 52.13\% & 52.13\% & 52.82\% & 51.88\% \\ VGG16 & 45.28\% & 45.94\% & 45.94\% & 47.18\% & 44.43\% \\ ResNet18 & 49.70\% & 49.81\% & 49.81\% & 51.74\% & 45.75\% \\ MobileNetV2 & 49.11\% & 49.33\% & 49.33\% & 50.94\% & 46.72\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Macro-average performance for testing on COVID-CT without fine-tuning. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Net** & **Pre** & **Rec** & **Spe** & **Acc** & **F1** \\ \hline VGG19 & 70.19\% & 68.70\% & 68.70\% & 69.15\% & 68.40\% \\ VGG16 & 61.19\% & 60.94\% & 60.94\% & 61.19\% & 60.84\% \\ ResNet18 & 70.16\% & 70.01\% & 70.01\% & 70.15\% & 70.02\% \\ MobileNetV2 & 67.13\% & 67.05\% & 67.05\% & 67.16\% & 67.07\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Macro-average performance for testing on COVID-CT with fine-tuning. less so. A deeper analysis of the results showed that many of the misclassified patients were classified as belonging to the class _pneumonia_. This issue is due to the similarity of the images, as both classes represent pneumonia, although of different origins. On the other hand, in the case of AlexNet, for the _normal_ class, a relatively high result was obtained, a sign that few patients were misclassified; a similar situation for the _pneumonia_ class where some patients were classified as belonging to both the _COVID-19_ and the _normal_ class. In general, the model obtained a balanced average accuracy. Additionally, GoogLeNet's results obtained for patients of the class _COVID-19_ are much lower than AlexNet, and even if the accuracy of the other classes is average, we can see how it affects the average accuracy of the network. As for InceptionV3, the accuracy of the _COVID-19_ class is higher than GoogLeNet but remains at a high level when compared to the results of the other classes. On the other hand, the average accuracy of the network obtains a positive result. VGG16 achieves 100% in the classification of patients in the class _pneumonia_, the highest result so far and the optimal one. The class _normal_ also obtained a very high result, while the class _COVID-19_, although obtaining outstanding results, had some minor difficulties in classification. As for VGG19, although it did not reach 100% in the _pneumonia_ class compared to VGG16, this model achieves high and uniform results: 95.91% in the _COVID-19_ class is the highest achieved so far. The other classes also produced satisfactory results, and the average accuracy of the network is 97.31%. ShuffleNet performs as well as the average, leading to a relatively low result for the _COVID-19_ class compared to the other two classes, which perform very well. However, network accuracy is still average. MobileNetV2 has excellent performance in the _pneumonia_ and _normal_ classes, while the poor result obtained in the _COVID-19_ class affects the average accuracy of the network. With regard to the Residual networks, ResNet18 obtained high results in the _normal_ and _pneumonia_ classes and remained average in the _COVID-19_ class. The network's final accuracy is also average; although both ResNet50 and ResNet101 achieved very high results in the two classes _pneumonia_ and _normal_, as ResNet18 did, those obtained in the _COVID-19_ class are drastically low, the lowest to date. This makes ResNet50 the network with the lowest average accuracy, caused mainly by the results obtained from the classification of patients in the _COVID-19_ class; ResNet101 is slightly higher, but still with unsatisfactory results.