# COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
## 4 Experimental Result Analysis
### Experimental Results  
For our experiments, we employed a dataset generated from three publicly available chest X-ray datasets [26; 30]. The combined dataset contains four different labels: Normal, Bacterial, Non-COVID Viral, and COVID-19. As the main goal of our study was to identify positive COVID-19 cases, we binarized the labels as either positive or negative. The entire dataset was then used to develop a COVID-19 detection model based on Convolutional Neural Networks (CNN). All chest X-ray scans in the dataset were resized to a standardized dimension of \(100\times 100\) pixels. The first layer of the CNN model served as the input layer, with a size of \(128\times 128\times 3\), representing RGB images. The subsequent layers consisted of Convolutional ReLU and Max Pooling layers, which were trained on thousands of images from the dataset. We then applied transfer learning by using pre-trained models on the COVID-19 dataset. The flattened layer was employed to convert the tensor to a vector, which was then passed to a fully connected neural network classifier. Figure 6: Representative X-ray chest images of patients with healthy (standard), COVID-19, and pneumonia diagnoses. To prevent overfitting, a dropout of 0.5 was applied. The output layer used the Adam Optimizer for continued optimization of the model. The original dataset was randomly divided into training and testing datasets using random sampling. Data augmentation was carried out during training, which included random rotations, translations, and flips of the training images. The model was implemented using the PyTorch toolbox. The batch size was set to 32, and each model was trained for 50 epochs, with these values determined empirically. The learning rate for the Adam optimizer was also determined empirically. The performance of the COVID-19 detection model was evaluated using a confusion matrix, which was obtained from testing over 200 accurate X-ray scans. The confusion matrix indicated that 147 scans were correctly classified as COVID-19 positive, and 45 scans were correctly classified as COVID-19 negative. The CNN-based COVID-19 detection model achieved an overall weighted F-measure of 97%, showcasing its effectiveness in identifying positive COVID-19 cases. The Rectified Linear Unit (ReLU) activation function was used for the model, which is beneficial due to its non-saturation property, leading to faster convergence during training compared to sigmoid or tanh functions. The ReLU function and its derivative are both monotonic, providing an output in the range of zero to infinite, making it suitable for classification tasks. The training parameters, including the number of layers, the shape of each layer, and the types of convolutional layers used, are summarized in Table 1.  
Additionally, we compared the performance of our COVID-19 detection model with other currently developed methods in Table 2. The model exhibited superior recall, precision, and F-measure metrics, making it stable and effective in identifying COVID-19 cases. This development contributes to providing expert radiologists in health centers with a valuable second opinion, improving diagnostic accuracy and aiding in the timely identification and management of COVID-19 cases. The performance of the developed CNN-based COVID-19 detection model is depicted in Figure 7. The figure shows the accuracy and loss trends during the training process. As observed, the accuracy of the model steadily increases with each epoch, demonstrating that the model effectively learns from the training data. On the other hand, the loss decreases significantly during the initial epochs, indicating that the model is reducing its prediction errors. The convergence of accuracy and loss further validates the effectiveness of the model in learning and generalizing from the training data. Overall, the performance of the CNN model is highly satisfactory, achieving a weighted F-measure of 97%, which confirms its potential to accurately identify COVID-19 cases from chest X-ray images.