## 5 Conclusions  
This study presents a deep-CNN-based transfer learning approach for the automatic detection of pneumonia and its classes. Four different popular CNN-based deep learning algorithms were trained and tested for classifying normal and pneumonia patients using chest x-ray images. It was observed that DenseNet201 outperforms the other three different deep CNN networks. The classification  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Task** & **Models** & **Accuracy** & \begin{tabular}{c} **Sensitivity** \\ **(Recall)** \\ \end{tabular} & **Specificity** & \
\begin{tabular}{c} **Precision** \\ **(IPV)** \\ \end{tabular} &
\begin{tabular}{c} **Area under** \\ **Curve (AUC)** \\ \end{tabular} & **F1 Scores** \\ \hline \multirow{4}{*}{Normal and Pneumonia} & AlexNet & 0.945 & 0.953 & 0.926 & 0.931 & 0.942 & 0.943 \\ \cline{2-8}  & ResNet18 & 0.964 & 0.97 & 0.95 & 0.954 & 0.963 & 0.965 \\ \cline{2-8}  & DenseNet201 & **0.98** & 0.99 & 0.97 & 0.97 & 0.98 & 0.981 \\ \cline{2-8}  & SqueezeNet & 0.961 & 0.94 & 0.98 & 0.985 & 0.96 & 0.961 \\ \hline \multirow{4}{*}{Normal, Bacterial Pneumonia and Viral Pneumonia} & AlexNet & 0.884 & 0.883 & 0.941 & 0.886 & 0.911 & 0.885 \\ \cline{2-8}  & ResNet18 & 0.877 & 0.88 & 0.94 & 0.875 & 0.91 & 0.909 \\ \cline{2-8}  & DenseNet201 & **0.933** & 0.932 & 0.967 & 0.937 & 0.95 & 0.935 \\ \cline{2-8}  & SqueezeNet & 0.861 & 0.859 & 0.93 & 0.87 & 0.895 & 0.865 \\ \cline{2-8}  & AlexNet & 0.90 & 0.94 & 0.845 & 0.86 & 0.89 & 0.921 \\ \cline{2-8}  & ResNet18 & 0.87 & 0.92 & 0.82 & 0.83 & 0.87 & 0.873 \\ \cline{2-8}  & DenseNet201 & **0.95** & 0.96 & 0.94 & 0.95 & 0.952 & 0.952 \\ \cline{2-8}  & SqueezeNet & 0.83 & 0.905 & 0.75 & 0.79 & 0.83 & 0.84 \\ \hline \end{tabular}
\end{table}
Table 4: Different performance metrics for different deep learning networks.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
accuracy, precision, and recall of normal and pneumonia images, bacterial, and viral pneumonia images, and normal, bacterial, and viral pneumonia were 98%, 97%, and 99%; 95%, 95% and 96%; and 93.3%, 93.7% and 93.2%, respectively. There are millions of children, who die each year due to this potentially fatal disease. Timely intervention with proper treatment plan through correct diagnosis of the disease can save a significant number of lives. Due to the huge number of patients in outdoor emergencies or emergencies specifically in the third world countries, medical doctors' time is limited and computer-aided-diagnosis (CAD) can save lives. Moreover, there is a large degree of variability in the input images from the X-ray machines due to the variations in the expertise of the radiologists. DenseNet201 exhibits an excellent performance in classifying pneumonia by effectively training itself from a comparatively lower collection of complex data, such as images, with reduced bias and higher generalization. We believe that this computer-aided diagnostic tool can significantly help the radiologist to take more clinically useful images and to identify pneumonia with its type immediately after acquisition. This fast classification will open up other avenues of application for this CAD tool, more particularly in the airport screening of pneumonia patients.  
Experiments were designed by [PERSON] and [PERSON]. Experiments were performed by [PERSON], [PERSON] and [PERSON]. Results were analyzed by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and M.A.K. All authors were involved in interpretation of data and paper writing. All authors have read and agreed to the published version of the manuscript.  
This work was made possible by NPRP12S-0227-190164 from the Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar and students' grant of Qatar University QUST-1-CENG-2020-23. The statements made herein are solely the responsibility of the authors.  
The authors declare no conflict of interest.