## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 from CXR Images using Ensemble of Deep Learning Models  
Ensemble learning involves capturing the outputs produced by different classifiers, which helps to ensure a robust prediction and increases the accuracy of deep learning models [38]. [PERSON] et al. [39] designed a model based on the performance of the following pre-trained deep learning models for the detection of pneumonia: AlexNet, DenseNet121, ResNet18, Inception-v3, and GoogLeNet. The dataset was obtained from the Guangzhou Women and Children's Medical Center (GWCMC); it consisted of a total of 5232 images labelled with the following classes: viral pneumonia, bacterial pneumonia, and normal. Random distribution of data into training (5232 images) and test (624 images) datasets was provided to avoid bias in performance. Some of the steps for data preprocessing include noise addition, random horizontal flip, and random-resized crop as augmentation techniques, with the images resized to 224 \(\times\) 224 pixels. The classification results from the pre-trained CNN models were combined into a prediction vector, and majority voting was applied to generate a final prediction output. The proposed deep learning framework of the ensemble model achieved 96.4% testing accuracy. [PERSON] et al. [40] compared the performance of deep hybrid learning (COVID-RENet-1 and COVID RENet-2) and deep boosted hybrid learning (COVID-RENet-1 and COVID-RENet-2) with the well-established CNNs (i.e., VGG-16/19, GoogLeNet, InceptionV3, ResNet-18/50, SqueezeNet, DenseNet-201, and Xception). Initially, the dataset images were resized to 224 \(\times\) 224 pixels, followed by data augmentation with the help of rotation, reflection, and scaling operations. The balanced public CXR dataset from the GitHub and Kaggle repositories was divided into 3224 COVID-19-infected and 3224 healthy cases. In order to reduce bias, the dataset images were randomly distributed into training and validation datasets, with 80% of data for system training and 20% for system validation. Within the deep boosted hybrid learning framework, transfer-learning-based fine-tuned deep COVID-RENet-1 and 2 were used for feature extraction, followed by eigenvector-based transformation using principal component analysis (PCA) and SVM as the final classifier. The authors observed that the deep hybrid learning models outperformed the well-established CNN models, while the deep boosted hybrid learning models performed the most effectively. [PERSON] et al. [41] also proposed an ensemble COVID-19-DeepNet system based on the integration of a deep belief network and a convolutional deep belief network. The purpose of the system was to differentiate between healthy and COVID-19-infected CXR images. A public dataset consisting of 24,000 CXR images was obtained from GitHub, Radiopneadia, the Italian Society of Medical and Interventional Radiology, the Radiological Society of North America, and Kaggle. A balanced dataset with 400 images for COVID-19 and 400 images for normal cases was used to obtain an augmented dataset with around 24,000 images of 128 \(\times\) 128 pixels in size. Some of the different operations used for data augmentation included random sampling of data into mutually exclusive training, contrast balancing using contrast-limited adaptive histogram equalization (CLAHE), noise removal with the help of a Butterworth bandpass filter, horizontal flipping, and five-degree rotation. To avoid bias in the results, the data were randomly distributed into training (75%), testing (25%), and validation (10%) sets. The results from two different deep learning approaches--namely, the deep belief network and the convolutional deep belief network--were fused together by computing predicted probability scores, and the final decision regarding classification was based on decision-level fusion. The proposed model was compared with the state-of-the-art approaches (e.g., COVID-Net, ResNet 50, COVID-ResNet, and EfficientNet-B3 model), and it outperformed other approaches, with a detection accuracy rate of 99.93%.