# Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models
## 4 Proposed Work
### Architecture of the Proposed Model  
This section briefly describes the different components of the proposed model for the prediction of COVID-19 cases.  
**VGG-16:** This architecture was proposed by [PERSON] et al. [29]. VGG-16 was one of the best performing architectures in the ILSVRC challenge 2014. The main specialty of this DCNN is its small kernel size. It uses a kernel of size 3 \(\times\) 3, which is repeated over 256 and 512 times in the layers. This helps the model to capture localized features peculiar to a particular class and thus improves the classification performance. There exists some drawbacks to using small kernel sizes in VGG architecture. As convolutions used in the VGG model are small, it increases the number of parameters to train. It also uses pooling layers at the appropriate position to eliminate irrelevant features and helps to decrease the complexity of the model.  
**ResNet50:** ResNet is a residual learning framework to ease the training of networks that are substantially deeper than those used previously [30]. The layers are explicitly reformulated as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. Residual can simply be understood as a subtraction of features learned from the input of that layer. This is done by creating alternate connections, which directly connects the \(n\)th layer to the \((n+x)\)th layer. This allows the propagation of dominant features much deeper into the network. It also helps to avoid the vanishing gradient problem. These residual networks are easier to optimize and can gain accuracy from considerably increased depth.  
**Xception:** The Xception model architecture [31] can be understood as an extreme customization of inception model architecture. In a traditional DCNN, each layer learns and transforms information obtained from its previous layer, but the usability of this information obtained can be highly influenced by the type of transformation performed. The Xception model takes advantage of this by applying convolutions using different kernel sizes and concatenates them and passes to the next layer. This allows the model to compute the most dominant features and eliminate others. It also maps spatial correlations and cross-channel correlations of the image separately, which is essentially equivalent to an existing operation known as a \"depthwise separable convolution\". This makes Xception a highly efficient architecture that learns about several distinctive characteristics and high-level features of the input data, which some simpler models might overlook.  
In the present work, all these three models are used with pre-trained weights and biases from the ImageNet dataset for the COVID-19 prediction. Few fully connected layers having rectified linear unit (ReLU) activation and a final classification layer having Sigmoid activation were added so that they work better on the dataset. These models are then used as base-learners in the stacking ensemble architecture.  
Here, we have aggregated the submodels, their predictions being blended together to give a better result than the individual predictions. We concatenated the individual models, thereby aggregating them into a single ensemble, and then further added two dense layers, one with ReLu and one with the Softmax activation function. Using CrossEntropy as the loss function, we then trained our stacked model.