## 5 Conclusions  
The proposed study uses a combined dataset of X-ray modality for chest disease detection by including a COVID-19 X-ray imaging dataset. To normalize the data, data augmentation is performed. This preprocessed data removes data bias, if there is any. However, a novel CNN is proposed for multi-chest disease detection. It saturates its learning in its training time by reporting an 87.89% validation accuracy. To increase the prediction results and reduce the prediction time, deep transfer learning is applied. In this way, self-activated deep features are extracted from the fully-connected layer of the proposed CNN. The deep features are fed to seven different domain algorithms of the ML approach. To make the proposed study more promising, two methods of validation (5- and 10-fold) are performed. It is observed from the results that more folding makes the results more accurate. This means that the proposed study can be used to detect multi-class chest \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Sr. No.**} & **Title of the Paper** & **Authors Names** & **Classifiers Used** & **Accuracy Achieved** \\ \hline \multirow{2}{*}{1} & Disease Staging and Prognosis in & \multirow{2}{*}{[PERSON] et al.} & \multirow{2}{*}{CNN} & \multirow{2}{*}{74.95\%} \\  & Smokers Using Deep Learning & & & \\ \hline \multirow{2}{*}{2} & Deep Learning for Screening & \multirow{2}{*}{[PERSON] et al.} & \multirow{2}{*}{(DL) (ML)} & \multirow{2}{*}{90.13\%} \\  & COVID-19 & & & \\ \hline \multirow{2}{*}{3} & A Deep Neural Network to & \multirow{2}{*}{[PERSON] et al.} & \multirow{2}{*}{CNN} & \multirow{2}{*}{87\%} \\  & distinguish COVID-19... & & & \\ \hline \multirow{2}{*}{4} & Chest CT manifestations of new & \multirow{2}{*}{[PERSON] et al.} & \multirow{2}{*}{GGO} & \multirow{2}{*}{98\%} \\  & coronavirus & & & \\ \hline \multirow{2}{*}{5} & Chest CT Findings in & \multirow{2}{*}{[PERSON] et al.} & \multirow{2}{*}{Ct scan} & \multirow{2}{*}{88\%} \\  & Coronavirus Disease-19... & & & \\ \hline \multirow{2}{*}{6} & Chest pathogens detection using & \multirow{2}{*}{[PERSON] et al.} & \multirow{2}{*}{Deep learning} & \multirow{2}{*}{87-94\%} \\  & deep learning & & & \\ \hline \multirow{2}{*}{7} & Chest disease radiography in & \multirow{2}{*}{[PERSON] et al.} & \multirow{2}{*}{CNN} & \multirow{2}{*}{97\%} \\  & two-fold & & & \\ \hline \multirow{2}{*}{8} & Pneumonia Detection on Chest & \multirow{2}{*}{[PERSON] } & \multirow{2}{*}{ML} & \multirow{2}{*}{95.8\%} \\  & X-Ra & & & \\ \hline \multirow{2}{*}{9} & Proposed & \multirow{2}{*}{Proposed} & \multirow{2}{*}{CNN, ML} & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Existing method comparison. disease detection. It not only increases accuracy, but also reduces the prediction time of a given testing sample. At last, a comparison shows the improvement of the proposed study from either single-class chest diseases or multi-class chest diseases, including COVID-19. In the future, multi-class decision-making CAD systems in different aspects of the medical domain should be used. However, data normalization needs to be considered to make the data reliable. Big data samples are encouraged for more confident results of chest diseases. The deep transfer learning features are also encouraged. Conceptualization, N.-u.R., M.S.Z., T.M., H.T.R., and R.D. ; Funding acquisition, A.M.E.-S. and M.A.E.-M.; Methodology, N.-u.R. and M.S.Z., T.M., H.T.R., R.D., A.M.E.-S., and M.A.E.-M.; Software, N.-u.R., M.S.Z., T.M., H.T.R., R.D., A.M.E.-S., and M.A.E.-M.; Visualization, H.T.R. ; Writing--original draft, N.-u.R., M.S.Z., T.M., H.T.R., R.D., A.M.E.-S., and M.A.E.-M.; Supervision, H.T.R. and R.D. All authors have read and agreed to the published version of the manuscript. The authors extend their appreciation to King Saud University for funding this work through Researcher Supporting Project (RSP- 2021/133), King Saud University, Riyadh, Saudi Arabia. Not applicable.