## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 in Chest CT and CXR Images Using Multimodal Deep Learning Models
The third stage was based on transfer learning from pulmonary nodule segmentation in CT scans to produce binary masks for segmenting similar regions in the given data. Ultimately, this method showed an accuracy of 99.4%, precision of 99.5%, recall of 99.1%, and F1-score of 99.3%. [PERSON] et al. [75] examined the effects of four deep learning models for the classification of COVID-19 using multimodal data consisting of CXR and CT images. The first stage of the proposed system is responsible for performing different image preprocessing functions, such as resizing, image augmentation (i.e., flipping, rotation, and skewing), and random data distribution of the total dataset (75,000 images) into training (70%) and validation (30%) subsets. Unfortunately, the authors did not provide the distribution of the dataset, limiting our ability to assess the level of balance in the dataset between the individual classes. The input images were resized to 224 \(\times\) 224 pixels, which is a standard size that is suitable for input to the different deep learning models used in this study. The second and third stages are responsible for deep feature extraction and image classification using the following four networks: VGG19-CNN, ResNet152-v2, ResNet152-v2 + gated recurrent unit (GRU), and ResNet152-v2 + bidirectional GRU (Bi-GRU). Of the four networks mentioned, the best results were provided by ResNet152-v2 + bidirectional GRU, with an accuracy of 98%, precision of 99.5%, recall of 98%, and F1-score of 98.24%. [PERSON] et al. [76] used the VGG-16 deep learning model for the classification of COVID-19. Before identifying the different lung infections, different preprocessing functions and data augmentation operations were performed to minimize the classification bias, enhance the system's generalizability, and improve the quantity and diversity of the data. Open-source data were acquired for this study, namely, the COVIDX CT-2A dataset, which includes 194,922 images from 3745 patients. From the open-source data, original data were used and randomly distributed into 80% for training and 20% for testing. The authors of this paper failed to highlight the class-wise distribution of the dataset used in their study, limiting our ability to assess level of class imbalance. Augmented images were converted into 512 \(\times\) 512 pixels before being used for model training and validation. For the classification of COVID-19, the proposed model was able to provide the best performance, with 99.2% accuracy, 99.6% precision, and 99.8% recall. A deep transfer learning algorithm was proposed in another study [77] to provide a rapid-response-based system for the classification of COVID-19 using multimodal data for CXR and CT images. Data from different publicly available sources were consolidated and utilized in this study. For example, a total of 6111 CXR images were acquired from two separate sources. Similarly, another data source was used for acquiring a total of 1252 CT scans. In the preprocessing stage, the original images were converted into 512 \(\times\) 512 pixels. The data were randomly split into training, validation, and testing subsets of 64%, 20%, and 16%, respectively. VGG-19 was used for transfer learning in this study; this deep learning model is based on 16 convolution layers and 3 fully connected layers, followed by 5 maximum-pooling layers and a softmax layer. The VGG-19 model is followed by the Grad-CAM model, which takes an image input to provide improved visualization output for detecting regions of interest. Once the predicted label has been calculated using the VGG-19 model, Grad-CAM is applied to the last convolutional layer of the VGG-19 model. Based on the different experiments performed, the best results obtained by the proposed algorithm provided an accuracy of 95.61%, precision of 88%, recall of 97%, and F1-score of 92%. In another study [78], two separate deep learning models were used for the classification of COVID-19.