# Deep Learning for COVID-19 Diagnosis from CT Images
## 5 Conclusions  
The objective of this work was to propose a classification methodology for the diagnosis of COVID-19 through deep learning techniques applied on CT images. To achieve this goal, an extensive comparative study of the main existing CNN architectures was carried out. The tests carried out on the two datasets showed very different results. Those obtained with the COVIDx CT-2A dataset are excellent for all the models used; in particular, VGG19 stands out for the high values obtained in the specificity metric and precision and recall. No other network has achieved these results. However, it is important to say that networks such as VGG16 and ResNet18 also achieved more than satisfactory results. As far as the other networks are concerned, GoogLeNet and ResNet50 seem the least suitable, as they \begin{table}
\begin{tabular}{l r} \hline
**Net** & **Acc** \\ \hline COVID-Net CT-1 [54] & 94.5\% \\ COVID-Net CT-2 L [54] & 98.1\% \\ COVID-Net CT-2 S [54] & 97.9\% \\ Bit-M [55] & 99.2\% \\ VGG-19 (this work) & 98.87\% \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of our work with the state of the art on COVIDx CT-2A. always deviated considerably from the average values obtained. In addition, the results obtained with VGG-19 are comparable with the results of the networks currently existing at the state of the art that works on COVIDx CT-2A. The patient-oriented classification also brought outstanding results, with high accuracy values for the class _COVID-19_, and, in some cases, 100% accuracy for the class _pneumonia_. The best network remains, in any case, VGG19, being the one with the highest average accuracy and, therefore, misclassifying a few patients compared to the other networks. Through the analysis of the misclassified patients, it was deduced that it is probably necessary to create an ad hoc network exploiting the existing CNNs to improve the results. About the COVID-CT dataset, however, the results do not match the previous ones and, on the contrary, there was a drop in performance of almost 50%. Only fine-tuning was able to remedy this, increasing the values obtained by 20%. Nevertheless, this does not compensate for the difference in performance. The problem could be mainly due to the quality of the images of the COVID-CT dataset, which are often compromised or of very poor quality. This work highlighted some limitations. First of all, cross-dataset experiments showed that existing CNNs, even after a fine-tuning procedure, really suffer from limited dataset scenarios. Second, patient-oriented experiments show that some networks misclassified some COVID-19 patients as normal pneumonia cases, while others did not. This clearly motivates further investigation on the models and, also, possible modifications. Third, the absence of defined standards in the acquisition of these images and, in addition, the problem of building affordable COVID-19 datasets from heterogeneous sources, especially during the early months of the pandemic [7] can be considered a limitation and also a future direction, as it clearly appears that the distinctive COVID-19 features need to be further studied. The indications emerging from this work are that:  
1. In addition to fine-tuning, some preprocessing steps oriented to the enhancement of CT images could be helpful for the networks to produce more discriminative features; and
2. Considering the results of the patient-oriented experiments, a hybrid approach, even involving ad hoc handcrafted features, could improve the results. In future directions, we certainly aim to discover other valuable features from CT images to recognise COVID-19, extending the investigation to include handcrafted features and even combining them with deep features. In addition, we also want to consider assessing the severity of COVID-19. We will conduct further experiments to identify key features in CT images and facilitate screening by medical doctors.