# Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
## 5 Experimental Results  
The test dataset contains 7395 images that have been prepared as a benchmark for the research community for the multi-classification task of detecting COVID-19 from CXR images. This dataset is collected from many links available on the \"kaggle\" website [43; 44; 45; 46]. The COVID-19 class of images are labelled by a licensed radiologist, and only those with a specific sign are used for the purpose of the study. A total of 7395 images were used (1371 for COVID-19, 1751 for normal, and 4273 for pneumonia) and Figure 3 shows samples of every class in the dataset. In this research, to precisely identify COVID-19, we employed a DL algorithm that utilized TL for the multi-classification of chest X-rays into three classes: COVID-19, normal and pneumonia. As can be readily seen, the performance measures of the proposed model for COVID-19 classification depends on different optimizers and different activation functions. The model was developed using the python programming language. Particularly, we used the TensorFlow open-source model, which is commonly used for machine learning applications, such as neural networks and Keras, a high-level model of neural networks built on top of TensorFlow. The proposed model was trained with a learning rate \(\eta\) of \(10^{-4}\), a batch size of 32 and categorical cross-entropy with RMSprop was used as the loss and optimization function. The training used 80% of the dataset, and validation and testing used 10% each. During model training, the loss curve for the training vs validation showed that the validation loss became low after a few epochs, see Figure 4, thus we select a number of epochs equal to 10 in our experiemnts. All images were resized to the target size of \(200\times 200\times 3\) with scale, and the augmentation principle was applied before being fed into the neural network. In addition to quantitative accuracy, the system performance against the number of epochs was analyzed. First, we needed to define the parameters used in the DL criteria. Furthermore, the training and validation loss was calculated as the sum of the errors made for each example in the validation or training sets. Generally, DL-based classification systems, including CNNs, utilize gradient descent (GD)-based optimization techniques to lower the error (or loss score) during the training process to adjust the network parameters. In this work, we have examined multiple optimization algorithms like Adam, RMSprop, SGD, AdaGrad, AdaDelta and AdaMax [47] and compared their performance. In the first set of experiments, we evaluated the performance using different optimizers to select the best network parameters for diagnosis with the LeakyReLU activation function at \(\alpha=0.1\) and \(\eta\) of \(10^{-4}\). The overall result accuracies for different optimizers are shown in Table 2. As can be readily seen, the performance measures of the proposed model for COVID-19 classification depends on the employed optimizer. Additionally, Table 3 summarizes the class-wise performance metrics of each optimizer in detail, and Figures 5 and 6 represent the confusion matrices and the ROC curves for different optimizers. Similarly, the system's performance against different optimizers using the ELU function has been also studied. Table 4 illustrates the performance metrics of the proposed model using ELU at \(\alpha=0.2\), and the associated confusion matrices are shown in Figure 7. Furthermore, Figure 8 displays the ROC curves for the examined optimizers, and the detailed class-wise performance metrics are given in Table 5.  
Figure 3: CXR examples for various classes from the test dataset. In addition to the evaluation of our system using different activations and optimizers, we evaluated the performance of our system against other DL methods. Particularly, we applied different pre-trained networks such as Inception V3, DenseNet121, ResNet50, MobilNet, and VGG16 on our dataset. The class identification was done by using the softmax layer of the pre-trained networks.