# Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks
## 4 Experimental Study
### Compared Methods  
In this section, the proposed methodology is analyzed systematically, and its positive and negative aspects are discussed in comparison with other methods in the literature. The obtained results in this study were compared with other studies that achieved successful results in the literature. This comparison is given in Table 3. The chest X-ray data set was used to compare the various advanced methods for pneumonia detection. The state-of-the-art methods of this data set are discussed as follows:  
* [PERSON] et al. [53] examined using Generative Adversarial Networks (GANs) to enrich a data set by producing chest X-ray data samples. GANs offer a method to learn about the underlying architecture of medical images, which can subsequently be used to make high-quality realistic samples.
* [PERSON] et al. [43] used transfer learning, which allows them to learn a neural network with a portion of the data required by traditional methods. They also made the diagnosis more transparent and understandable by highlighting the neural network's known areas.
* [PERSON] and [PERSON] [31] employed two well-known CNN approaches, Xception and Vgg16. In the learning phase, they employed transfer learning and fine-tuning.
* [PERSON] et al. [28] proposed a CNN-based method. Unlike other methods based solely on transfer learning or traditional handcrafted techniques, they trained the CNN model from scratch to extract attributes from a given chest X-ray image to achieve remarkable classification performance. They used it to determine if a person was infected with pneumonia or not.
* [PERSON] and [PERSON] [36] performed pneumonia detection with a CNN model architecture using residual connections and dilated convolution methods. They also discovered the transfer learning effect on CNN models when classifying chest X-ray images.
* [PERSON] et al. [54] proposed an automatic transfer-learning method based on CNN's using DenseNet121 pretrained concepts.  
Figure 4: Confusion matrices of the chest X-ray data set.  
The proposed ensemble method showed better performance than a pretrained CNN model. In addition, designing a CNN model needs massive experiments and knowledge to pretrain the CNN model. According to our test results, the proposed ensemble method was shown to have better performance than a pretrained CNN model. Figure 4 shows a performance comparison of the proposed MobileNetV2, DenseNet169, VIT, and the proposed EL method. In addition, designing a CNN model needs massive experiments and domain knowledge to train a pretrained CNN model with transfer learning. Further, CNN models trained from scratch need more data, more training time, and more epochs to gain better generalization ability on input data.  
However, the proposed method suffers from two drawbacks: The first is defining hyperparameters of pretrained CNN methods while applying TL and fine-tuning to a problem of one's own. TL requires determining an appropriate pretrained CNN method for a related issue, the size of fully connected layers, and the number of freezing layers. Many researchers use the trial-and-error approach or their own experiences to identify these parameters. Therefore, finding out TL parameters can reveal lengthy trial-and-error methods. The second drawback of the proposed EL method needs to have a lot of variance and bias.