# Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks
## 2 Related Works  
Over the past decade, many researchers have automatically used deep learning to detect lung infections and diseases from chest X-ray. For example, CheXNet is a 121-layer CNN-based approach developed by [PERSON] et al. [27]. This approach was trained using 100,000 chest X-ray images from 14 different diseases. The approach was also applied using 420 chest X-rays, and the results were compared with those of radiologists. Therefore, it was found that the DL-based CNN method outperformed the average performance of radiological pneumonia detection. In [28], they trained a CNN method from scratch to retrieve features from chest X-ray images to achieve excellent classifier performance and used it to detect whether or not a patient had pneumonia, in contrast with previous studies based on traditional manual features. [PERSON] et al., in [29], suggested a method based on adaptive average filtration CNN and random forest to predict pneumonia using chest X-ray images. The adaptive filtration was applied to remove noise from the chest X-ray image, improving accuracy and making it easier to identify. Then, using dropout, a CNN model with two layers is created for extracting features. Nevertheless, more preprocessing with the adaptive filter is required to enhance CNN's classification accuracy. However, there are some issues with CNN models, which require a large amount of data with labels to be trained. Furthermore, learning a CNN architecture is computationally expensive and requires advanced machines. As a result, a transfer learning (TL) approach has been proposed to solve these problems. Recently, the TL method has become very popular, mainly because it enables the CNN model to be more efficient, have reduced costs, and require fewer inputs [30]. [PERSON] and [PERSON] [31] used the Xception and VGG16 structures to fine-tune transfer learning. The design of Xception was substantially altered with the addition of two fully linked levels and multiple-output tiers with a SoftMax activation mechanism. As per the theory, the channel's initial layer has the greatest generality potential. The previous eight layers of the VGG16 architecture have been stopped, and the fully linked levels have been altered. Similarly, the test period for each image was 16 ms for VGG16 and 20 ms for the Xception network. In [32], the methods included InceptionV3, ResNet18, and GoogLeNet. Classifier results were merged using the strong majority in this method. This means that the diagnosis goes with the group with a high proportion of first-time voters. Averaging out the model's testing results, this approach took 161 milliseconds per image. On top of that, they were able to classify chest X-ray images with great accuracy. Pneumonia may be detected using deep CNNs, as per the results of this research. We use standard algorithms as a component in our approach to categorizing data to keep computation costs minimum. [PERSON] et al. [33] used transfer learning techniques on ImageNet to detect pneumonia using four pretrained CNN architectures. They used three classification strategies to classify chest radiography images. [PERSON] et al. [34] utilized three well-known CNN models for extracting features in the pneumonia classification task. They used the same data for training each model individually and acquired 1000 features from every CNN's last fully connected layer. For this task, these features are essential, which was reduced by the minimum redundancy maximum relevance (mRMR) feature selection method. Moreover, the selected features were fed into machine learning (ML) classification algorithms. [PERSON] et al. [35] suggested a CapsNet architecture for diagnosing pneumonia in chest X-ray images using multilayered capsules. [PERSON] and [PERSON] in [36] suggested a new residual network-based trained TL approach for pneumonia diagnosis. Further, the DL model used in their study had 49 convolutional layers and 2 dense layers. Their model had a 90.05% test accuracy.