## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 in Chest CT Images Using Deep Learning Models  
Multiple studies have used chest CT scans to detect and classify COVID-19 in im-ages. CT images are chosen due to the ease of detecting abnormal regions in infected lungs, along with their accuracy in extracting features of pneumonia. However, in practice, CT scans are not the most suitable option for COVID-19 diagnosis due to the limited availability of the necessary equipment and the high cost associated with the process. In addition, both the American College of Radiology and the Italian Society of Radiology (SIRM) do not recommend chest CT scans as a screening tool for COVID-19 [59]. Based on these guidelines, many studies refrain from using CT images as the input modality for the classification of COVID-19. Consequently, this subsection of the paper considers studies that ultimately decided to rely on CT scans as the primary modality for data collection for the development of their deep-learning-based models for the classification of COVID-19. [PERSON] et al. [60] designed a slice-level classification model for three learning tasks--segmentation, classification, and image reconstruction--for CT scan images. A CNN model was used, the architecture of which consists of a common encoder and two decoders based on U-Net. A common encoder module was used for the three tasks, taking a CT scan as its input, and its output was then used for image reconstruction through the first decoder module, followed by the segmentation task completed by the second decoder module and multi-class classification for COVID-19 (and other lung diseases) performed by the multilayer perceptron. Different state-of-the-art models were compared to this classification model; the authors used AlexNet, VGG-16, VGG- 19, ResNet-50, 169-layer DenseNet, InceptionV3, Inception-ResNet v2, and EfficientNet. The dataset was collected from three hospitals and contained 1369 images divided into three classes: 425 for normal cases, 449 for COVID-19 cases, and 495 for other infections. In order to avoid classification bias, the original balanced dataset was randomly distributed into training (1069 images), validation (150 images), and test (150 images) sets for performance evaluation. The preprocessing stage involved the conversion of original images down to 256 \(\times\) 256 \(\times\) 3 pixels and \(512\times 512\times 3\) pixels, and these two sizes were used as inputs to the proposed models, with the smaller images providing a comparatively better performance. The proposed model outperformed state-of-the-art methods for the classification and segmentation of COVID-19, achieving an accuracy of 94.67% for the classification task and a dice coefficient of 88.0% for the segmentation task. [PERSON] et al. [61] developed a 3D deep learning framework using the architecture of COVNet to identify COVID-19 from CT scan images. The initial input data of 3D CT scans were preprocessed to reduce the original dimensions to 512 \(\times\) 512 \(\times\) 3 pixels, followed by the extraction of the region of interest (i.e., lungs) using a U-Net-based segmentation model. The preprocessed image was given as an input to COVNet to extract visual features from 2D local and 3D global images. The COVNet framework leverages ResNet-50 as the backbone model for deep feature extraction from CT slices, which are combined using a maximum pooling operation, followed by a fully connected layer and softmax activation function to generate a probability score for classification (COVID-19, CAP, and non-pneumonia). The study used a dataset obtained from six hospitals, amounting to 4356 CT scan images divided into three classes: 1296 for COVID-19, 1735 for CAP, and 1325 for non-pneumonia. To remove classification bias, the original balanced dataset was randomly distributed into 3918 CT images for model training and 434 CT images for model testing. The proposed framework achieved an AUC of 96% for the identification of COVID-19 and 95% for CAP on CT scan images.