# Pneumonia Recognition by Deep Learning:
###### Abstract
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Contributions** & **Limitations** \\ \hline [PERSON] et al. [8] & The research achieved pneumonia localization and recognition in chest X-ray images based on a deep learning approach. & There was no corresponding comparative assessment of pneumonia recognition accuracy and computational efficiency. \\ \hline [PERSON] et al. [16] & The research proposed two models for the employed dataset with deep learning following a lightweight architecture. & A comparison with the ResNet model was performed only for the classification of COVID-19 and with a small dataset. \\ \hline [PERSON] et al. [17] & The research proposed a self-supervised deep neural network which could achieve better recognition without requiring a large quantity of labeled training data. & The results were compared on different datasets, but not with other deep learning model approaches \\ \hline [PERSON] et al. [18] & The research proposed an efficient deep learning model for COVID-19 recognition, the COVID-DeepNet model. & The model was designed for small datasets only, and it is not possible to judge the model’s pneumonia recognition under large datasets. \\ \hline [PERSON] et al. [19] & The research proposed the “deep fractional max pooling neural network (DFMPNN)” model, which obtained a higher accuracy of pneumonia recognition. & The dataset was relatively small and more advanced pooling techniques could be tested. \\ \hline [PERSON] et al. [20] & The research developed a reliable convolutional neural network model for performing X-ray image classification of COVID-19. & The model comparison analysis was limited, only three types of model were compared. \\ \hline [PERSON] et al. [21] & The research classified X-ray images of COVID-19, SARS, and MERS using deep convolutional neural networks. & Diverse datasets were collected, but the datasets were still small and no operations to expand the datasets, such as data augmentation, were used. \\ \hline [PERSON] et al. [22] & The research proposed a Bayesian optimization-based convolutional neural network model for pneumonia recognition in chest X-ray images. & Comparative analysis with other models in terms of computational efficiency was lacking. \\ \hline [PERSON] et al. [23, 24, 25] & The research proposed three deep learning methods for COVID-19 pneumonia detection with high stability and accuracy. & The dataset needed further processing to improve the model’s performance. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of related studies. models in this paper, contributes to the selection of deep learning models for pneumonia recognition in different situations. The analysis will be beneficial to further improve the efficiency and accuracy of pneumonia recognition. The contributions of this paper can be summarized as follows. 1. Five classical deep learning models are compared, and the advantages and disadvantages of each model are analyzed. 2. The computational accuracy and efficiency of each model in different situations is compared. 3. Suggestions are given for the selection of deep learning models in different situations. This is beneficial for the rapid selection of suitable deep learning models in practical applications for pneumonia recognition to improve efficiency. The rest of this paper is organized as follows. Section 2 describes the materials and methods in detail. Section 3 analyzes the comparison of the results in this paper. Section 4 provides discussion as well as considering possible future research. Section 5 concludes the paper.