## 5 Recommendations to Bridge the Gap  
Based on the limitations of prior studies and the gaps identified by this literature review, this section provides recommendations covering different aspects that can help researchers in this area. These recommendations are as follows:  
* For problems based on radiological imaging modalities, deep learning has achieved remarkable successes in feature learning and image classification. The architecture of \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**Data Used**} & \multirow{2}{*}{**Sample Size**} & \multirow{2}{*}{**Dataset Balance**} & \multirow{2}{*}{**Balance**} & \multirow{2}{*}{**Model Type**} & \multirow{2}{*}{**Classification Method**} & \multirow{2}{*}{**Performance**} \\  & & & **Balance** & & & & \\ \hline \multirow{9}{*}{[PERSON] et al. [78]} & \multirow{9}{*}{CXR/CT-scan} & 1345 & & & & & \\  & & CXR & & & & & \\  & & COVID-19, & & & & & \\  & & 1345 & & & & & \\  & & 1345 & & & & & \\  & & CXR normal, & & & & & \\  & & 1345 & & & & & \\  & & & CXR & & & & \\  & & pneumonia, & & & & & \\  & & & & & & & \\  & & CT COVID-19, & & & & & \\  & & 1345 & & & & & \\  & & CT pneumonia & & & & & \\ \hline [PERSON] et al. [79] & CXR/[PERSON] et al. [79] & 377,110 CXR & N/A & Not mentioned & Multi-modal & Multi-class & Accuracy = 92\% \\  & report & 377,110 CXR & N/A & Not mentioned & Multi-modal & Multi-class & AUC = 90\% \\ \hline \multirow{9}{*}{[PERSON] et al. [80]} & CT-scan/Symptoms/Exposure history & 486 COVID-19, & & & & & \\  & & & 486 negative & Yes & - & Multi-modal & Binary & AUC = 92\% \\  & COVID-19 & & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [81]} & CT-scan/Clinical information & 70 COVID-19, & & & & & \\  & & 66 non-COVID-19, & & & & & \\  & & 19, & & & & & \\ \hline \multirow{9}{*}{[PERSON] et al. [82]} & CT-scan/Clinical information/Lab Tests & 88 COVID-19, & & & & & \\  & & 80 other pneumomias & & & & & \\ \hline \multirow{9}{*}{[PERSON] et al. [83]} & CT-scan/Clinical information/Lab Tests & & & & & & \\  & & 689 cases & Yes & - & Multi-modal & Multi-class & Accuracy = 99\% \\ \cline{1-1}  & & & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [84]} & CXR/Clinical data & 222 COVID-19, & & & & & & Accuracy = 97\% \\  & data & 48 normal & No & Augmentation techniques & Multi-modal & Binary & Recall = 98.6\% \\  & & & 48 normal & No & Resampling techniques/ & & & Precision = 97.8\% \\  & & 290 COVID-19, & & & & & & F-Score = 98.2\% \\  & & 180 viral, & & & & & \\  & & 130 normal & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [86]} & CT-scan/Clinical data & 176 COVID-19, & & & & & \\  & & 76 normal, & & & & & \\  & data & 60 CAP. & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [87]} & CXR/Clinical data & Total data = & & & & & \\  & data & 1834 patients & N/A & Not mentioned & Multi-modal & Binary & C-index = 0.805 \\ \hline \multirow{2}{*}{[PERSON] et al. [88]} & CXR/Clinical data & Total data = & & & & & Accuracy = 73.2\% \\  & data & 5645 cases & N/A & Not mentioned & Multi-modal & Binary & Recall = 70.7\% \\ \cline{1-1}  & & & & & & & Precision = 71.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)CNNs has resulted in their status as pioneers in the field of image classification and detection [89]. These systems can detect and learn what radiologists cannot notice using the naked eye, and they significantly outperform traditional techniques, even in previously impossible cases [32]. * Referencing the superior results achieved by hierarchical classification compared to flat classification in previous studies--as shown in the literature--and due to the natural hierarchical structure of diseases developed by the ICD-10 [69], hierarchical classification can improve performance compared to flat classification [27]. * Multimodal deep learning models that combine different types of data in a process of data fusion are more accurate compared to single-modality models, especially in medical research.