## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 from CXR Images using Ensemble of Deep Learning Models
[44] utilized deep ensemble learning via a proposed network that consisted of two stages of pre-training followed by the actual deep learning architecture developed specifically for the recognition of COVID-19, which provided an input CXR of 256 \(\times\) 256 pixels in size. The first stage of pre-training was composed of different deep learning models (i.e., ResNet18, VGG-16, VGG-19, Xception, Inception-V3, Dense-Net-121, MobileNet-V2, and NasNet-Mobile) with pre-trained weights on an ImageNet dataset, followed by successive layers for zero-padding, fully convolutional layers, pooling layers, dropout, and softmax activation. The second stage of pre-training was based on the first-stage pre-training model, followed by additional pooling, dropout, and softmax activation layers. The two stages of pre-training were concatenated with additional pooling and activation layers to provide the final recognition results. The ensemble learning leveraged the top three, top five, and top seven results from the proposed models with majority voting, simple averaging, and weighted averaging to obtain the final results. A total of 720 CXRs from the Montreal COVID-19, Twitter-COVID-19, RSNA, CheXpert, and NIHcollections were used in this study. These data were randomly split into 80% for training and 20% for validation to avoid bias in the performance of the proposed system. The proposed system with deep ensemble learning was able to achieve an accuracy of 90.97%. The proposed deep stacked ensemble in [45] requires the preprocessing stage to reduce the size of the original images to 224 \(\times\) 224 pixels, which is given as input to the feature extraction module leveraging different deep learning models (i.e., ResNet, MobileNet, Inception, DenseNet, and NasNet). The two models with the best performance are stacked to form a deep ensemble model for COVID-19 prediction. The dataset used in this study contained a total of 2905 images (219 COVID-19 images and 2686 normal class images), which were randomly divided into training (80%) and test sets (20%) by maintaining consistency in class labels at each partition and minimizing classification bias. However, the overall class imbalance was present throughout the training data. The proposed stacked ensemble was able to provide the best accuracy result of 95.1% on the given dataset. The development of COVID-Net and COVID-EnsembleNet was discussed in [46]. The COVID-Net architecture was based on four pairs of consecutive convolution and maximum-pooling layers, with filters ranging from 16 in the first pair to 128 in the fourth pair. These convolutional layer blocks were followed by the flattening operation and two fully connected layers with 128 and 2 filters, where each fully connected layer employed the softmax regression activation function. The COVID-EnsembleNet was constructed using the proposed COVID-Net architecture along with the existing VGG-16 architecture. The dataset used in this study contained 1281 positive cases and 3269 negative cases, with a random distribution of data into training (3641 images), testing (455 images), and validation (455 images) sets to avoid classification bias. Some of the preprocessing functions included image resizing to 224 \(\times\) 224 pixels, image normalization for faster network convergence, and data augmentation techniques such as random horizontal and vertical image flipping, followed by a random 72-degree rotation. The proposed ensemble model was able to provide binary accuracy of 99.56% and multiclass accuracy of 97.56%. A novel ensemble deep learning model for the detection of COVID-19 from CT images was developed in [47], utilizing 2500 lung CT images from COVID-19 patients, along with 2500 CT images of lung tumors and 2500 CT images of normal lungs from a hospital. These images were randomly distributed into 6000 images for training and 1500 images for validation.