## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 from CXR Images using Ensemble of Deep Learning Models
In another study, [PERSON] et al. [42] proposed a hybrid model that exploits the potential of deep learning and transfer learning techniques to develop accurate and robust models for detecting COVID-19. The preprocessing function involved transforming the dimensions of the original dataset into 120 \(\times\) 120 pixels. Three basic blocks were used for building seven different variants. These building blocks included (1) a CNN block, (2) a transfer learningblock using VGG16 or VGG19, and (3) a machine learning block. A publicly available dataset obtained from Kaggle was used with a private dataset from Asir Hospital, Abha, Saudi Arabia, which consisted of a total of 4103 CXR images labelled as COVID-19, viral, or normal. To avoid bias in the performance, a random distribution of data into training (3279 images) and validation (820 images) sets was performed. The hybrid model using VGG-16 with transfer learning was able to outperform all other variants, with an accuracy of 97.6%. [PERSON] et al. [38] applied the Choquet integral for ensemble deep learning models and evaluated the fuzzy measures for each classifier using coalition game theory (Shapley value), information theory, and lambda fuzzy approximation. Some of the preprocessing functions involved downscaling of original images to 512 \(\times\) 512 pixels using bicubic interpolation and color-space translation to convert the original dataset images from RGB to grayscale. For deep feature extraction, three standard deep learning models with pre-trained weights on the ImageNet dataset were used--namely, VGG-16, Xception, and Inception-v3. The authors obtained a public dataset of CXR images from two repositories (namely, GitHub and Kaggle), which was used to create a single dataset called the Novel COVID-19 Chest X-ray Repository. The dataset consisted of 752 COVID-19-infected, 1584 viral pneumonia, and 1639 normal CXR images. Random sampling of images was used for the training, validation, and testing datasets, with the ratio of training, testing, and validation data as 77%:20%:3%, respectively. Finally, the results were combined to generate a final output as one of the following three classes: COVID-19, pneumonia, or normal. The ensemble model outperformed many recent methods, with an area under the curve (AUC) of 0.97 and a validation accuracy of approximately 98.99%. Another method used ECG data for the classification of COVID-19 [43]. The proposed automated tool consisted of four steps: ECG trace image preprocessing, deep feature extraction and feature incorporation, hybrid feature selection, and classification. In the preprocessing stage, the dimensionality of the input image was reduced from the original image (ranging between 952 \(\times\) 1232 and 2213 \(\times\) 1572 pixels) to 224 \(\times\) 224 pixels for ResNet-50, ShuffleNet, and MobileNet and 229 \(\times\) 229 pixels for Inception-v3, Xception, and Inception-ResNet. The dataset consisted of 250 scans of cases with COVID-19, 848 for trace records of cases with a present or former myocardial infarction and irregular heartbeat, and 859 normal images. To avoid the classification bias arising from class imbalances, the number of images per class was kept equal (i.e., 250 images per class) for the binary and multi-class classification. The proposed method for hybrid feature extraction utilized 10 different DL-based approaches. These networks include Inception-ResNet, ResNet-18, ResNet-50, ShuffleNet, Inception-v3, MobileNet, Xception, Dark-Net-19, DarkNet-53, and DenseNet-201. The fully connected deep features obtained from the different networks were followed by hybrid feature selection utilizing a forward search with a random forest classifier. The performance of the proposed system was compared with ResNet-50, ShuffleNet, and MobileNet to reveal the superior performance of the proposed method. [PERSON] et al.