## 5 Materials and Methods  
This section explores the detailed description of our proposed framework regarding COVID-19 detection. This research study has designed a generalized detection framework, as shown in Figure 4, describing the entire process of this experiment. The acquirement of the data images from the aforementioned imagery datasets (Dataset 1 (formed on X-ray images), Dataset 2 (formed on X-ray images), and Dataset 3 (formed on CT scan images)) denotes the commencement of the framework proposed in this research work. Such images are passed on to the pre-processing module of the proposed framework that is standardized and allows for the integration of the data from the various datasets to carry out various pre-processing mechanisms for improving the accuracy and performance of our proposed architecture. As a part of image pre-processing, data augmentation was employed in order to improve the amount of data available during training and to incorporate diversity into the training data that helps to overcome the problem of overfitting. The data augmentation technique in this work consists of flip augmentation and rotation augmentation which increase the number of data samples. The state of images after performing augmentation and transformation is shown in the following figures (Figures 5 and 6). The other preprocessing steps used during model building are reducing the size of images, cropping the image, flipping, rotating, and rescaling which helped in creating variances which lead towards building a stable end-model. After data pre-processing, dataset splitting commences and during this phase, the datasets are divided into two different parts--training datasets and test datasets. Each dataset is divided into training and test datasets using a 70%-30% distribution and K-fold cross-validation (K = 10). The present research performs the tuning process of three existing models, namely VGG-19, ResNet-50, and Inception-V3 during the training process thereby producing superior results with a view to detect COVID-19. The VGG-19 is a 19-layer convolutional neural network architecture (including 16 convolution layers, 3 fully connected layers, 5 MaxPool layers, and 1 SoftMax layer). The prime objective of developing this network is  
Figure 4: Proposed framework for COVID-19 detection using imagery data. Figure 5: State of images after flip augmentation. Figure 6: State of images after rotation augmentation. to increase the number of hidden layers while sinking the number of parameters. In this architecture, a fixed kernel size of 3 \(\times\) 3 is perceived, and as the progress along the network is achieved, the number of kernels continues enhancing in the hopes of a better feature. ResNet-50 containing 50 layers is a deep residual network. A residual function along with traditional CNN is used by ResNet-50 to solve the problem of gradient dispersion and accuracy degradation. If the process of the addition of a number of layers in our network is continued, during backpropagation, the model will not be able to spread the weight changes due to gradient explosion or dissipation. The ResNet-50 model comprises of five stages, each with a residual block. These residual blocks are responsible for keeping parameters as low as possible. This provides better training and testing errors even if the network depth increases and provides regularization. Hence, ResNet-50 is regarded as the most popular CNN architecture in terms of image classification. Inception-V3 architecture follows the concept of factorized convolutional which reduces computational efficiency. The reduction in the filter size helps in achieving a faster training speed thereby minimizing the number of parameters. This model includes auxiliary classifiers which act as regularizers and are employed to ensure convergence of very deep neural networks. An auxiliary classifier is added in between the layers during training and the losses incurred are added back to the main network.