# Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks
## 5 Conclusions  
This paper proposes a CNN Ensemble Learning (EL) method for automatically identifying normal and pneumonia patients in chest X-ray images. For this purpose, the three most successful CNN models (DenseNet169, MobileNetV2, and Vision Transformer) were selected for the proposed EL method from among the trained CNN models. The proposed method generated the results during the testing stage. Further, a global average pooling layer was merged after the convolutional layers to avoid losing spatial information in the image. In the classifier stage of the proposed method, fully connected layers were used. As a result, it was found that incorporating these capabilities enhances the classification performance of each CNN model. Consequently, the proposed EL method achieved satisfactory classifier performance using the chest X-ray data set. In future studies, we plan to build a weighted ensemble method based on the CNN model's accuracy.  
Conceptualization, A.M., R.P.D.R., A.D., M.K. and M.A.E.; methodology, A.M., R.P.D.R. and A.D.; software, A.D., A.M. and M.A.E.; validation, A.D., A.M., M.K. and M.A.E.; formal analysis, A.M., R.P.D.R., A.D., M.K. and M.A.E.; investigation, A.M., R.P.D.R., A.D., M.K. and M.A.E.; writing--original draft preparation, A.M., R.P.D.R., A.D., M.K. and M.A.E.; writing--review and editing, R.P.D.R., A.D., M.K. and M.A.E.; visualization, A.M., R.P.D.R., A.D., M.K. and M.A.E.; supervision, R.P.D.R., A.D., M.K. and M.A.E.; project administration, R.P.D.R., A.D., M.K. and M.A.E.; and funding acquisition, R.P.D.R. and M.K. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method/Ref.** & **Accuracy (\%)** & **Year** \\ \hline DCGAN/[53] & 84.19 & 2018 \\ \hline
[43] & 92.80 & 2018 \\ \hline VGG16/[31] & 87.00 & 2019 \\ \hline
[28] & 93.73 & 2019 \\ \hline
[36] & 90.50 & 2020 \\ \hline DenseNet121/[54] & 86.80 & 2021 \\ \hline EL/Our & **93.91** & **2022** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative accuracy results for the state-of-the-art method on test set of the chest X-ray data set. The best results for each item are labeled in bold.  
**Funding:** This work has received financial support from the European Regional Development Fund (ERDF) and the Galician Regional Government, under the agreement for funding the Atlantic Research Center for Information and Communication Technologies (atlanTTic). This work was also supported by the Spanish Government under re-search project \"Enhancing Communication Protocols with Machine Learning while Protecting Sensitive Data (COMPROMISE)\" (PID2020-113795 RB-C33/AEI/10.13039/501100011033).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The selected dataset in this study is available on this link: [[https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia)]([https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia)) (accessed on 4 January 2022).  
**Conflicts of Interest:** The authors declare that there are no conflict of interest regarding the publication of this paper.