## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 in Chest CT and CXR Images Using Multimodal Deep Learning Models
This was a higher result compared to the use of a single modality (unimodality), which achieved 75.5% accuracy on clinical features alone, 67.7% accuracy on lab test features alone, and 90.8% accuracy on CT scan data alone. A number of different deep learning models were used in another study, and their performance was compared to highlight the most suitable alternative for the classification of COVID-19 [84]. The private dataset was collected from King Fahad University Hospital, Dammam, KSA, and consisted of 270 cases. The preprocessing step was able to reduce the image size to 224 \(\times\) 224 pixels (for case 2) and 300 \(\times\) 300 pixels (for case 3), followed by random data distribution into 80% training and 20% validation sets. Different data augmentation techniques were employed, such as flipping (both horizontal and vertical), rotation, shifting, cropping, blurring, zooming, rescaling, and shearing. Three different cases were used in this study for selecting the most suitable deep learning model for the classification of COVID-19. Case 1 used clinical data for the training and validation of a 13-layer deep learning model. Case 2 used CXR data for training and validation on a diverse range of CNN architectures (i.e., ResNet, DenseNet, VGG19, EfficientNet). Case 3 used multimodal data (i.e., clinical and CXR images) with transfer learning to learn weights for EfficientNet, which was used as a backbone architecture for the classification of COVID-19. For case 3, which used multimodal data, the proposed system was able to provide the best performance (accuracy of 97%, recall of 98.6%, precision of 97.8%, and F1-score of 98.2%). Similarly, [PERSON] et al. [85] used multimodal data of symptoms and CXR images for the development of a deep-learning-based model for the classification of COVID-19. The public dataset contained a total of five classes: bacterial, COVID-19, non-COVID-19 viral, initial-stage COVID-19, and normal. The total dataset was subjected to random splitting into an 80% training set and 20% testing set. The images in the publicly available dataset were converted into 150 \(\times\) 150 pixels as one of the main preprocessing steps. For the symptom data, the preprocessing step involved the removal of duplicate rows and null values and the application of resampling techniques to address the class imbalance issues. This step was followed by the training and validation of the preprocessed data using logistic regression and CNN models. For image data, different data augmentation techniques--zooming, rotation, and translation--were performed during CNN training. The CNN model was trained on the transformed images, and the decision tree model was trained on the labeled results of the previous two trained models to provide the final output, with an accuracy of 78.88%. A fully automated hybrid framework based on capsule networks (CT-CAPS) and random forest classifiers was used in another study for the classification of COVID-19 using chest CT images and clinical/demographic data [86]. Private CT scans and the associated clinical/demographic data were collected for a total of 312 patients (176 COVID-19 patients, 60 pneumonia patients, and 76 normal cases) in this study. The dataset was randomly split into training (60%), validation (10%), and testing (30%) subsets. A capsule-network-based framework--namely, CT-CAPS--was used in this study, consisting of a stack of convolutional, pooling, batch normalization, and capsule network layers to extract slice-level feature maps from CT images in the first stage of the proposed model. The second stage of the proposed model leveraged the maximum pooling output of the first stage, followed by a conventional multilayer perceptron for final classification. The proposed model was able to provide 90.8% accuracy, 94.5% precision, 86% recall, and an AUC of 0.92. [PERSON] et al. [87] leveraged deep learning models for the classification and severity prediction of COVID-19; a total of 1834 patients' CXR images and clinical data were used.