## 6 Conclusions  
The objective of this research was to develop a classification methodology for COVID-19 diagnosis using deep learning techniques on chest X-ray images. To attain this goal, a customized unsupervised CNN model was proposed to detect COVID-19 using chest X-ray images early. The data used for training, validation and testing were preprocessed for the model to enhance the intricacies within the X-ray images. Cross-dataset studies are performed to evaluate the resilience of the proposed model in a practical situation. Two publicly available datasets were used to train and validate the model. Specifically, the model was generated from nine layers to provide accurate and efficient results. The nine layers consist of four convolution layers to generate nonlinear feature maps, three max-pooling layers to extract local data and decrease the size of generated feature maps from convolution layer to prioritize important features in image and two dense (ANN) layers to classify between COVID-19 and normal X-ray images. The accuracy, precision, recall and F1-score reached by the customized CNN model for COVID-19 and normal chest X-ray images were 98.5%, 98%, 98% and 98% respectively. A comparison table was designed to compare the number of samples and accuracy with other models. In comparison to other methods with and without self-attention, our method delivered greater accuracy with fewer parameters and produces results in less time providing efficient COVID-19 classification task performance. The findings of the experiments showed that our method outperformed as compare to other COVID-19 screening methods. COVID-19 has already taken millions of lives worldwide and still poses a risk to populations. Our healthcare systems are struggling to save lives and healthcare staff are overworked to tackle this  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **COVID-19 Samples** & **Normal Samples** & **Class** & **Accuracy** & **Preprocessing** \\ \hline Xception CNN [20] & 287 & 1349 & Multi & 91\% & ✔ \\ \hline DL-CRC [21] & 209 & 27,228 & Multi & 93.94\% & ✔ \\ \hline CNN [23] & 278 & 278 & Binary & 89.7\% & - \\ \hline ConvNet [24] & 250 & 500 & Binary & 97.78\% & - \\ \hline Sequential CNN [29] & 206 & 206 & Binary & 97.56\% & ✔ \\ \hline CheXNet [30] & 165 & 165 & Multi & 99.7\% & ✔ \\ \hline Customized CNN (this work) & 400 & 400 & Binary & 98.5\% & ✔ \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison table.  
situation. An automated diagnosis system provides faster and easier screening predictions. We hope that our proposed model will be highly impactful in high-risk areas where lack of resources and diagnosis facility remain deficient as the proposed model has the ability to become a candidate in detecting COVID-19 early in hospitals.  
Conceptualization, M.S.F.; data curation, S.H.R.K.; formal analysis, A.U.R., M.I., M.A.R., J.F.A.-A. and M.M.; investigation, J.A., M.S.F.; methodology, M.S.F.; project administration, M.S.F. and M.M.; resources, M.S.F. and S.H.R.K.; software, M.S.F.; supervision, M.S.F., J.F.A.-A. and M.M.; validation, M.I.; visualization, M.A.R.; writing--original draft, M.S.F., A.U.R. and S.H.R.K.; writing--review and editing, J.A., M.M., M.S.F. and S.H.R.K.; proofreading and writing the paper script in overleaf, M.S.F. and J.A. All authors have read and agreed to the published version of the manuscript.  
Taif University Researchers Supporting Project number (TURSP-2020/211), Taif University, Taif, Saudi Arabia.  
Not Applicable.  
The authors are grateful for the support of Taif University Researchers Supporting Project number (TURSP-2020/211), Taif University, Taif, Saudi Arabia.  
The authors declare no conflict of interest.